Comparing Upscaling Models: RealESR_Gx4, RealESR_Animex4, BSRGANx4, BSRGANx2, RealESRGANx4, IRCNN_Mx1, and IRCNN_Lx1

Introduction

Image upscaling models have evolved to address a variety of content types and degradation issues. In this report, we compare several prominent upscaling models – RealESR_Gx4, RealESR_Animex4, BSRGANx4, BSRGANx2, RealESRGANx4, IRCNN_Mx1, and IRCNN_Lx1 – in terms of their output quality and suitability for different content. We focus on how each model handles natural photographic images, cinematic/video stills, anime-style content, video game textures, and blurry image restoration. We emphasize visual quality, detailing strengths and weaknesses such as sharpness, naturalness, and typical artifacts, rather than runtime performance. A summary table is provided at the end for quick reference.

Upscaling Model Overviews

RealESR_Gx4: This is a 4× upscaling model targeting general (real-world) images. It uses the SRVGGNetCompact architecture – a lightweight network optimized for faster inference while still providing good quality. RealESR_Gx4 is essentially a compact version of Real-ESRGAN’s general model, sacrificing some fine detail in exchange for speed and stability. It produces clean results with relatively fewer artifacts, but the outputs can be slightly softer and less detailed compared to larger GAN-based models. This model is well-suited when a balanced, natural upscale is needed quickly (for instance, upscaling many photos or videos on modest hardware), and it serves as the default in some upscaling apps due to its speed. Typical artifacts are minimal; if anything, overly smooth areas might result due to the smaller network capacity.

RealESR_Animex4: This is a 4× upscaling model specialized for anime content. It was fine-tuned on anime images/videos and uses a similar compact architecture optimized for anime line art and flat colors. RealESR_Animex4 preserves the crisp lines and color regions characteristic of anime or cartoons, avoiding the blurring or color bleed that general models might introduce. It excels at cleaning and upscaling low-resolution anime frames with minimal flicker and artifacts, as the model was explicitly optimized for anime video sequences. However, it is not well-suited for realistic photos – applying it to natural imagery can yield a “cartoonish” look or flattened textures since the model will tend to simplify shading and details to match an anime style. In its domain (animation frames, game sprites, comics), it produces sharp and artifact-free results, whereas outside that domain it should be avoided.

BSRGANx4 & BSRGANx2: BSRGAN (“Blind Super-Resolution GAN”) is a GAN-based upscaler designed for real-world blind degradation scenarios. It doesn’t assume a specific blur or noise pattern in the input; instead, it was trained with a wide range of degradations (random blur kernels, noise, compression, etc.) to handle unknown real-world conditions. The 4× model (BSRGANx4) upscales by a factor of 4, while BSRGANx2 upscales by 2 (useful when a moderate enlargement is needed or to upscale in two steps). BSRGANx4 is known to excel at enhancing fine details like text and sharp edges – it often makes letters, textures, and object boundaries crisper and more legible. Its approach tends to introduce a grain-like “staticky” detail into the image, as users have noted; this means the output isn’t overly smooth or plastic-looking, but rather retains (or adds) a bit of noise/grit which can resemble natural film grain or texture. This grainy detail can be an advantage for preserving a natural look, but in some cases it may appear as noise if overdone. BSRGANx2 works similarly but with a smaller upscale, often yielding slightly cleaner results due to the lower magnification. Overall, BSRGAN models are slightly older and arguably less sophisticated than the latest Real-ESRGAN models, but they remain strong performers for many restoration tasks, especially where maintaining or enhancing edge detail is crucial.

RealESRGANx4: This 4× upscaling model is the flagship of the Real-ESRGAN family, built on the RRDB (Residual-in-Residual Dense Block) architecture with GAN training (an evolution of ESRGAN). It was optimized for real-world image restoration, meaning its training included diverse degradations (blur, noise, downsampling, compression) to handle practical low-quality inputs. RealESRGANx4 produces very sharp and high-detail outputs – it can reconstruct convincing details in textures, faces, and complex scenes, even adding plausible details in blurry areas thanks to its generative adversarial training. It also has a special capability for face enhancement (the RealESRGAN framework can be combined with GFPGAN for faces), making it effective for old photos or low-res portraits. However, this strength can also be a weakness: RealESRGANx4 sometimes over-sharpens or invents details that appear unnatural. Users have observed that it can create “random line” artifacts in fine textures (for example, water or fur rendered with unnatural streaks), and generally a more “cartoonish” or exaggerated look if pushed too far. These are essentially sharpening artifacts, as one user noted – the model “emphasizes all the edges” which can lead to a halo or etched effect around details. Despite this, RealESRGAN is widely regarded as one of the best general-purpose upscalers for natural images, balancing detail enhancement with a relatively natural appearance. It’s excellent for photographs, realistic paintings, and even many video game scenes, provided one is mindful of the potential for overshoot in detail.

IRCNN_Mx1 & IRCNN_Lx1: These are 1× (no scale) image restoration models focused on denoising (and mild deblurring) rather than upscaling. They originate from the IRCNN framework (circa 2017), which provided pretrained CNN models for image restoration (likely from the paper “Learning Deep CNN Denoiser Prior” by Zhang et al., 2017). In the context of this comparison, IRCNN_Mx1 is the “medium” denoising model, and IRCNN_Lx1 is the “large” (strong) denoising model. They do not enlarge the image but are used to clean heavy noise or artifacts from an image before or after upscaling. IRCNN_Mx1 will remove moderate noise while preserving more detail, and IRCNN_Lx1 targets heavier noise levels – at the cost of potentially smoothing out fine detail. Over-application of the strong model can lead to the “plastic look” often associated with aggressive noise reduction: surfaces and skin may become waxy and lose natural texture if all grain is stripped away. These models’ strength lies in restoring clarity in very noisy or compressed images (for example, cleaning up a grainy film scan or JPEG-blocky video frame). They are less useful for sharpening or adding detail – in fact, they tend to slightly blur edges as a side effect of noise removal, especially the Lx1 model. In summary, IRCNN_M and L are supporting tools: use them to pre-process or post-process images in a pipeline (e.g. denoise first, then upscale with a GAN model, or upscale then apply a touch of denoising to reduce GAN noise). They ensure a cleaner final result but must be used judiciously to avoid wiping out important details.

Performance by Content Type

1. Natural Photography (Realism)

For high-resolution restoration of natural photographs, RealESRGANx4 and BSRGANx4 are the primary contenders. Both were designed for real-world photo enhancement and can handle complex, non-cartoon images well.

• RealESRGANx4 often delivers the sharpest and most detailed results on real photos. It excels at recovering textures in landscapes, faces, and objects. For example, an old family photo with faces and foliage would come out crisper and more detailed with RealESRGAN. It also includes specialized handling for faces (reducing blur and improving facial detail) which can be beneficial for portraits. However, one must watch out for over-sharpening – RealESRGAN can sometimes introduce halos or unnatural lines when it “guesses” details, making the photo look slightly hyper-real or accented. Fine repetitive patterns (like water ripples, grass, or hair) might look artificially lined or too clean due to these GAN hallucinations. Despite that, for most natural images it produces an impressive enhancement, turning a low-res or mildly blurred photo into something that looks high-res and clear.

• BSRGANx4 provides a slightly different balance. It tends to preserve a more natural texture by introducing grain-like details. On a natural photograph (say a street scene or an animal photo), BSRGAN will sharpen edges and make features like text signs or fur stand out clearly. Users have noted that BSRGAN’s outputs can look as if a fine layer of grain or “static” was added to the image. This can actually be an advantage for realism: the subtle noise can mimic film grain and avoid the overly smooth or “plastic” appearance. BSRGAN is also often better at keeping small text or numbers legible than overly smoothing them. The downside is that if one prefers a very clean look, BSRGAN’s grain may be undesirable – the image might appear slightly noisy or rough in flat areas like skies or skin. In extremely noise-sensitive cases, a quick denoiser after BSRGAN (or using RealESRGAN instead) might be preferable. But overall, for natural photos, BSRGAN is a strong choice when you want crisp detail without the risk of unnatural line artifacts that RealESRGAN might produce. In fact, some workflows combine them: e.g., use BSRGAN to establish detailed texture, then a light pass of a diffusion or refinement model to clean any grain.

• RealESR_Gx4 plays a supportive role for realism. Being a lighter model, it does not introduce the same level of new detail as the GAN-based models. On a natural photo, RealESR_Gx4 will produce a clean upscale with relatively natural smoothness. It’s less likely to produce odd artifacts because it’s not pushing the image as hard. This makes it safer for very challenging images where strong GAN enhancements might amplify compression blocks or sensor noise. For example, if you have a mildly blurry smartphone photo, RealESR_Gx4 will upscale it decently, reducing some blur and noise gently. The result may not “pop” with sharpness as much as RealESRGAN’s output, but it also won’t have weird textures. It’s a good default when you need a quick and safe enhancement. You might rate its suitability for natural images as Medium: it’s good, but not the top performer in pure resolving power. Use it when RealESRGAN outputs are too artifact-prone for your taste, or if you need to process many images quickly with consistent, artifact-free results.

• RealESR_Animex4 is unsuitable for realistic photos (Low suitability). Applying the anime-specific model on a real-life photo can result in loss of texture and a stylized look – skin might become flat, shading simplified, and natural noise/grain wiped clean. It may even add an outline effect to objects or posterize the colors, as it’s trying to interpret the photo as if it were animation. In short, avoid the anime model for realism; its strengths (line clarity, flat fills) are the opposite of what a photo needs.

• BSRGANx2 (2×) on natural images can be useful if you only need a moderate enlargement or want to upscale in stages. The x2 model might introduce slightly less GAN artifact per step. Some workflows upscale 2× with BSRGANx2 then another 2× with BSRGANx2 (or with another model) to reach 4×, aiming to reduce aggressive artifacts. BSRGANx2 will still enhance edges and detail, but compared to x4 it could retain a bit more of the original image’s character (since each upscale step is smaller). Generally, if 4× output from BSRGANx4 looks too gritty or oversharpened, using BSRGANx2 (or RealESR_Gx4) might yield a more natural 2× result.

• IRCNN_Mx1 / Lx1 are helpful for noisy natural photos. If a photograph is not only low-res but also high-noise (for example, a high-ISO night shot), you can first apply IRCNN_M or L to clean the noise, then upscale with one of the above models. The medium denoiser is often enough for grainy photos; it can make the subsequent upscaling easier by removing background noise. The strong denoiser (Lx1) should be reserved for extremely noisy images (or scans) because it will flatten finer details along with the noise. In general, these denoisers don’t add sharpness – they actually blur tiny detail – so they are not used alone for enhancing a photo’s clarity. Instead, think of them as prep tools: High noise -> run IRCNN (especially L) -> then use RealESRGAN or BSRGAN to sharpen and upscale. This two-step approach can yield a very clean yet detailed result: denoiser removes the noise, GAN upscaler restores edges and textures.

Artifacts & Visual Qualities (Realism): RealESRGAN and BSRGAN each have distinct artifact tendencies on photos. RealESRGAN might produce over-sharpened “etched” edges or plastic smooth areas, especially in homogeneous regions like skies or water (giving a slightly fake appearance if overdone). BSRGAN might produce noisy spots or speckles – a “pointillist” detail that some describe as static noise – but this can also be interpreted as fine detail. RealESR_Gx4 might simply look a bit softer or lower contrast by comparison, with fewer artifacts. The IRCNN models can cause over-smoothing (plastic/waxy look) if overused. In terms of sharpness vs naturalness, RealESRGAN leans toward sharpness (risking naturalness), BSRGAN tries to be sharp with some natural noise (often a good compromise), and RealESR_Gx4/IRCNN lean toward naturalness (smooth, artifact-free, but not as sharp).

2. Cinematic Content (Film/Video Stills)

“Cinematic” content – e.g. frames from movies, TV shows, or live-action video – poses some unique challenges. Such content often contains film grain, compression artifacts, or stylistic color grades. Upscaling these frames demands preserving the artistic intent (like grain structure or depth of field blur) while enhancing clarity. It also involves maintaining consistency across frames if processing video. Here’s how the models compare:

• RealESRGANx4 is a powerful choice for film stills, as it can greatly improve clarity of each frame. For example, an old 480p movie frame upscaled with RealESRGANx4 to 1080p or 4K will gain noticeable detail – soft background details become more distinguishable, and facial details or text in a scene become sharper. RealESRGAN’s ability to handle complex degradations means it will tackle typical video issues (minor motion blur, noise, compression) in one go. However, an important consideration for video use is temporal consistency: RealESRGAN, applied frame-by-frame, does not inherently ensure that details persist or do not “flicker” between frames. Since it’s an image model, it might invent slightly different textures frame to frame (especially in grain or noise), which can result in a subtle flicker in the upscaled video. For cinematic content where grain is an artistic element, RealESRGAN might also suppress natural grain and replace it with its own texture, altering the filmic look. Some users have found RealESRGAN’s output on video can be a bit “too cartoony” or clean for live-action film if over-sharpened. To mitigate this, one might apply a gentle grain or noise addition after upscaling to restore a film-like texture, or blend the upscaled frame with the original to keep some grain. In summary, RealESRGANx4 offers High enhancement for cinematic frames, but with a moderate risk of temporal artifacts and over-cleaning.

• BSRGANx4 on cinematic content tends to preserve a bit more of the original grain and noise structure, due to its grain-injecting tendency. If upscaling a classic film or a dark, grainy scene, BSRGAN might actually be preferable to keep the atmosphere. It will sharpen the scene (good for improving perceived resolution of faces or set details) and enhance edges (for example, making film subtitles or credits clearer). The added “static” detail can blend with existing film grain, sometimes yielding a very natural result that looks like a higher-resolution scan of the same film. The risk is that BSRGAN might amplify grain too much, turning fine film grain into more obvious noise. Also, like RealESRGAN, it’s an image model with no built-in temporal consistency, so it can introduce slight flicker (especially if the “static” detail it adds differs each frame). In practice, users often consider BSRGAN High suitability for older anime or CGI content in videos and Medium for live-action: it’s great at crisping edges but one must watch for noise buildup. If the source is a clean modern digital video (with little noise), BSRGAN’s output may appear a bit rougher than RealESRGAN’s, so choose based on whether maintaining some grit is desired.

• RealESR_Gx4 might be a smart choice for upscaling longer videos or films when you want to avoid processing artifacts. Its faster speed and lighter touch mean it’s less likely to create flicker from hallucinated details – each frame is upscaled more predictably. Additionally, because it’s not a GAN, it might preserve the original grain or noise pattern more faithfully (it won’t “fight” the grain as much as a GAN that tries to clean the image). For example, an 80s movie with heavy grain upscaled by RealESR_Gx4 could retain much of that grain, just finer, whereas RealESRGAN might partially smudge it out. The downside is lower sharpness – fine details won’t be enhanced as dramatically. So, content type suitability is Medium: if artifact-free stability is priority (e.g., archiving video with original character intact), RealESR_Gx4 is good; if maximum enhancement is the goal, the GAN models are stronger.

• IRCNN_Mx1 / Lx1 are particularly handy for denoising or de-artifacting video content before upscaling. Many cinematic sources (DVD rips, older TV recordings) have compression blocks or sensor noise in dark scenes. Running an IRCNN pass can significantly improve these: e.g., IRCNN_M will tidy up mild grain or blockiness without destroying the scene detail too much, and IRCNN_L can aggressively clean very noisy footage (old analog video, for instance). After denoising, an upscaler like RealESRGAN has an easier job and is less likely to latch onto noise as if it were detail. One common use-case: use IRCNN_Lx1 on a particularly grainy or low-light scene to avoid the upscale model producing flickering noise, then upscale with RealESRGAN. This combination yields a cleaner, more consistent video (though as noted, too much denoising can give a waxy look, so apply only as needed). In summary, IRCNN models have High usefulness for video pre-processing, but by themselves they don’t sharpen or upscale.

• RealESR_Animex4 generally should not be used on live-action or cinematic content (Low suitability). It’s tailored for flat colors and line art; when fed a movie frame, it may interpret film grain or fine detail as “noise” and remove it, or it might overly smooth out textures and give the frame a slight cel-shaded appearance. The exception might be if you have a film that is actually animated (Japanese anime film, or a cartoon movie) – in that case, using the anime model would be ideal (that scenario belongs in the anime category, though). For any standard cinematic content (actors and real scenes), keep the anime model off.

• BSRGANx2 for video: similar to photos, the 2× version could be useful if you plan a two-stage upscale for quality reasons or if you only need a small boost (e.g., converting 720p to 1440p). BSRGANx2 might introduce fewer temporal inconsistencies at each step than a single 4× step, so some videographers upscale in two passes. Each pass being milder can reduce flicker (though doubling frames and then doubling again is slower). If RealESRGANx4 is too slow or aggressive, an alternative pipeline is: denoise (optional) → BSRGANx2 → BSRGANx2 (again) or RealESR_Gx4 to reach the final resolution.

Artifacts & Visual Qualities (Cinematic): Upscaling film/video often faces flicker as a key artifact. Neither RealESRGAN nor BSRGAN guarantees temporal coherence, but they tend to produce different artifact styles: RealESRGAN may cause a flicker of sharpening halos or some frames where a detail “pops” more than others, whereas BSRGAN may cause a flicker in grain patterns. If a scene has subtle film grain, BSRGAN’s output might dance a bit as the noise pattern it generates changes per frame. RealESRGAN might instead flicker when it aggressively sharpens an edge differently frame-to-frame. Using the lighter RealESR_Gx4 or a pre-denoiser can mitigate both by reducing how much random detail is added. In terms of sharpness vs naturalness: often for cinematic content, naturalness (preserving the film look) is crucial, so one might accept a slightly softer upscale if it means no weird artifacts. BSRGAN tends to preserve that feel better (grain and all), whereas RealESRGAN tends to clean up the image making it look modern and crisp – sometimes at the expense of the original mood. Thus, the choice can depend on the project: for a restoration of a classic film, BSRGAN or a careful RealESR_Gx4 + grain retention might be best (to keep it looking like film but sharper). For a boost to a low-res modern video, RealESRGAN might produce a stunningly clean HD image from SD source, suitable for viewing on high-res displays, so long as a bit of loss in original texture is acceptable.

3. Anime Content

When it comes to anime (including hand-drawn 2D animation, cartoons, and game art in anime style), the requirements are quite different: preserving line art, flat colors, and avoiding adding textures that weren’t originally there. Here, specialized models shine:

• RealESR_Animex4 is explicitly designed for this domain and offers High suitability. It was trained on anime content and even optimized for anime video upscaling (with attention to temporal coherence). On a typical anime image or frame (think of a screenshot from a 90s anime TV episode or a small manga panel scan), RealESR_Animex4 will sharpen and clarify lines, smooth color regions (where appropriate), and recover details like patterned fills or fine outlines that may have been lost at lower resolution. Importantly, it does this without introducing realistic textures – meaning it won’t try to add skin pores or fabric grain that wasn’t drawn, keeping the upscaled image faithful to the original art style. It also tends to remove compression artifacts from digital anime (e.g., banding in flat colors or blockiness) while keeping the intended shading. If used on anime videos, it produces stable results (less flicker on line art) because the training included sequential frames. In summary, for anything anime or cartoon, RealESR_Animex4 is usually the first choice. Artifacts are minimal in this mode: you might only notice occasional oversimplification (if the model overly smooths a very textured effect that was meant to be there, like film grain over animation or deliberate noise). But it rarely produces weird artifacts; it’s tuned to make output look like higher-res anime, not something else.

• RealESRGANx4 (general) can also be applied to anime if the specialized model is not available. It will improve resolution and detail to a degree, but because it’s not fine-tuned for flat artwork, it might handle certain elements less gracefully. For example, RealESRGANx4 might introduce slight ringing artifacts around sharp line art (a faint halo or double edge) due to its sharpening nature. It could also interpret a very flat gradient as something that needs texture, adding a bit of noise or unevenness where the anime model would keep it flat. On simple anime images, RealESRGAN still does well, but on complex ones (with lots of fine lines, text, or dithered shading), you may catch some aliasing or moiré patterns after upscaling. Another risk is color shifting – GAN models sometimes alter colors slightly; the anime model likely learned to maintain the exact color fields. So while RealESRGANx4’s suitability for anime is Medium, it’s a decent fallback that delivers sharpness, just be prepared for minor artifacts and possibly a less “authentic” look compared to using the anime-specific model.

• BSRGANx4 is usually not recommended for pure anime/cartoon images (Low suitability). Its strength – adding grain and detail – becomes a weakness here. Anime typically has large areas of solid color or simple gradients, which BSRGAN might mistakenly identify as lacking detail and thus try to “fix” by adding texture or noise. The result could be an upscaled anime frame with a noisy fill in what should be a flat area (e.g., a character’s hair might end up with speckles, or a blue sky with static grain). It will also sharpen lines, which might seem good, but it could over-sharpen them to the point of jaggedness or jaggy artifacts. For text (like subtitles or captions in anime-style), BSRGAN would make them sharp but might introduce a rough edge compared to the cleaner upscale from an anime-tuned model. There are specialized anime upscalers (like waifu2x, etc.), and RealESR_Animex4 among our list is one – so one typically wouldn’t use BSRGAN unless they specifically want a gritty, textured look applied to the animation (which is rare). In modding communities, sometimes people applied ESRGAN/BSRGAN to background art from games or anime to add realism, but for authentic anime preservation, it’s not ideal.

• RealESR_Gx4 on anime content yields a fair result (Medium suitability). It will upscale lines and shapes without adding the wrong textures, since it’s not a GAN. However, because it isn’t specialized for anime, it might not restore some line details as crisply as RealESR_Anime does. Fine contours could end up a tad softer. Still, RealESR_Gx4 doesn’t generate noise, so flat areas remain flat – that’s good. If you have a lower-spec scenario and need to upscale anime quickly, RealESR_Gx4 will do a reasonable job maintaining clarity. It might just lack that last bit of sharpness on edges and may not resolve very small line details that the anime-specific model would catch. Essentially, it’s safe but not optimal for anime: no major artifacts, but not the sharpest or most accurate either.

• BSRGANx2 is similarly not suited for anime; a 2× version doesn’t change the fundamental mismatch of the method to the content. If anything, doing 2× increments with a non-anime model could reduce obvious artifacts slightly, but better to use an anime-centric model outright.

• IRCNN_Mx1 / Lx1 have limited application in anime. Most anime images are clean (unless you’re dealing with noisy analog captures or very old animation cels). If you do have a noisy anime image – say a screen capture with compression noise or an old cartoon with grain – a light denoising pass could help before upscaling. IRCNN_M could remove mild compression artifacts (blocking or color banding noise) before feeding frames to RealESR_Animex4, making the upscale even cleaner. However, one must be careful: too much denoising on anime can erase thin lines or texture that are intentional (for example, the grain in a stylistic scene or the outline details). Generally, anime content doesn’t need heavy denoising beyond what the specialized upscalers already handle. So IRCNN models are low relevance here, except in special cases of noisy sources.

Artifacts & Visual Qualities (Anime): The key is to avoid creating new textures. The anime-specific model is very good at this – it avoids artifacts and retains a hand-drawn look. RealESRGAN might cause slight aliasing or ringing on lines (sharpening artifacts) and sometimes a bit of unwanted texture, but still far less than something like BSRGAN would. BSRGAN will likely produce the most noticeable artifacts (noise on flats, jaggies on lines), making the image look messier than the original. In terms of sharpness vs naturalness, for anime “naturalness” means fidelity to the original art style. RealESR_Animex4 offers the best balance: high sharpness and natural cartoon style. RealESRGAN might oversharpen (higher sharpness, lower fidelity), and BSRGAN oversharpens plus adds grain (sharpness with even less fidelity). So, the recommendation is clearly to use the anime-trained model for both image and video anime upscaling. The outputs will be crisp without the “plastic” issue (since anime doesn’t have natural texture to become plastic-looking – the risk instead is oversmoothing, which the model avoids by design).

4. Gaming Textures

“Gaming textures” can refer to a couple of scenarios: upscaling in-game assets (like texture files for a mod), or upscaling game screenshots. Often, it means the former – enhancing textures from older games to higher resolution for use in mods. These textures might be realistic (photographic) or stylized (pixel art or cartoonish), and can include UI elements. Key requirements are preserving important details (like text on signs, or interface icons clarity) and avoiding artifacts that could stand out when tiled in a game.

• BSRGANx4 has proven quite popular in the modding community for texture upscaling. Its knack for sharpening text and edges makes it ideal for things like game UI, menus, and any texture with alphanumeric characters (for example, signs, buttons with labels, etc.). It can take a blurry game texture of a wall with bricks and make the edges of the bricks more defined and even hallucinate some finer grain on the bricks, which increases perceived detail. The grainy detail it adds can help tiled textures look more varied and less obviously low-res repeated. For instance, an old game texture for ground or concrete, when upscaled with BSRGAN, will get some roughness that looks like realistic grit when rendered in-game. Artifact risk: BSRGAN could introduce noise in places you might not want it. If a texture is supposed to be uniformly smooth (say a clean metal panel in a sci-fi game), BSRGAN might add an uneven texture that wasn’t intended. Additionally, very small or thin features in a texture (like a single-pixel width line in a sprite) might become jagged or fuzzy if the model misinterprets it. Overall though, for a wide range of game textures, BSRGAN yields a satisfying mix of sharpness and added detail – one might rate its suitability High for realistic textures and Medium for very clean/cartoon textures.

• RealESRGANx4 is also used for game asset upscaling, especially when a less noisy result is preferred. It will create a smoother upscale than BSRGAN – which can be beneficial for textures that need to remain clean (for example, a character’s face texture in an RPG: RealESRGAN can upscale it and even add some plausible facial detail, whereas BSRGAN might add noise that looks like freckles or skin blemishes that weren’t originally there). For environment textures like grass or wood, RealESRGAN will bring out details but might sometimes make them a bit too smooth or “glossy”, a phenomenon modders sometimes describe as a “plastic” or overly clean look. This is because RealESRGAN might average out some roughness while trying to keep things coherent, especially if the input was very low quality. Still, it generally maintains structural details well – e.g., cracks in a stone texture will be enhanced and sharpened, not lost. Suitability: likely High for many textures, especially those involving organic or face details (where you don’t want random noise), and Medium for things where you actually welcome a bit of grit (where BSRGAN might outperform). Notably, RealESRGAN has been the go-to for many because it’s more recent and often yields fewer obvious artifacts when the goal is a believable upscale. If BSRGAN’s extra detail appears as unwanted noise in-game, RealESRGAN’s output might blend more naturally with modern graphics.

• RealESR_Gx4 can serve when a very neutral upscale is desired. If you have a texture that is already quite clean and just needs resolution boost with minimal change (for example, upscaling a high-contrast pixel art sprite or a UI icon), RealESR_Gx4 will do that reliably. It won’t invent much detail, which means it also won’t screw up the original look. It’s also faster, so if a modding pipeline requires processing thousands of textures, using the lightweight model could save time. The downside is that it may leave some blurriness if the original was very blurry, since it lacks the powerful detail-generation of GANs. So a logo or text that’s barely readable in the original might come out still not very sharp after RealESR_Gx4, whereas RealESRGAN or BSRGAN could have made it legible. Thus, use cases: ideal for simple upscales of things that are mostly flat or already vector-like, or as a second-stage after a GAN (some modders do GAN upscale then a pass of a simpler model to clean residual noise). Suitability Medium overall – not the top choice for adding detail, but safe and useful for preserving stylistic integrity.

• BSRGANx2 could be part of an approach if one wants to be cautious. For instance, upscale textures 2× with BSRGANx2 to add some detail but not too much noise, then perhaps use another 2× (either BSRGANx2 again or RealESR_Gx4) to reach 4×. This might yield a more controlled result. If direct 4× with BSRGAN is too harsh, the 2× route is an option. Similarly, BSRGANx2 is great for when you only need a slight bump (like taking a 512px texture to 1024px) – it’ll sharpen it a bit without overdoing it. In comparisons, artifact risk is lower per upscale step with the 2× model.

• RealESR_Animex4 can be relevant if the game textures are anime-style or pixel art. Some games, especially Japanese ones or indie pixel art games, have assets that are essentially anime drawings or sprite art. The anime model would treat those very well, maintaining clean lines and color blocks. For instance, upscaling 2D character sprites or visual novel backgrounds (which are often anime art) is a perfect scenario for RealESR_Animex4 – you’d get a crisp result with no random texture. Using a general or BSRGAN model on such assets might add unwanted detail (like noise on what should be a flat cell-shaded area). So while not applicable to all games, for cartoon-like game graphics, the anime model’s suitability is High. Conversely, on realistic game textures, it’s Low (just as with photos, it would flatten or simplify details). So one should choose based on the art style of the game.

• IRCNN_Mx1 / Lx1 might be used in a texture workflow if the originals are extremely noisy (perhaps scanning artifacts or existing poor upscales). Generally, game textures themselves aren’t “noisy” – they might be low-res or compressed, but not film-grain noisy. A scenario could be: if you have a heavily JPEG-compressed texture atlas, running a denoiser can remove blocking artifacts before upscaling. Or if the game was early 3D with dithering patterns in textures, denoising might smooth that out. The medium denoise would be enough in most cases, since strong denoising could wipe out small texture details that you want to keep (like the fine pattern on a fabric texture). So IRCNN is a niche tool here – Medium suitability only in special cases of artifact removal prior to upscaling, otherwise usually not needed.

Artifacts & Visual Qualities (Gaming Textures): In a game, upscaling artifacts can be very noticeable when textures tile or when UI elements need to be crisp. BSRGAN artifacts: a bit of noise can actually enhance realism for textures like stone or wood, but can be a downside for UI/text (grainy text icons). RealESRGAN artifacts: occasionally a GAN “swirl” or checker pattern might appear on very low-res textures if it misinterprets something (for example, it might see a random pattern in a blurry texture and create a weird repetitive detail – though this is not frequent). More often, RealESRGAN might produce a too-smooth result (which isn’t exactly an artifact, but might look like a plastic version of a surface). Modders sometimes describe ESRGAN outputs as “wax museum” look for faces if over-smoothed. RealESR_Gx4 will mostly just suffer from blurriness if it fails to add detail, but not generate new artifacts. So if sharpness is paramount, it may fall short, but it won’t produce something ugly – you just might still see some blurry areas.

Overall, the sharpness vs naturalness trade-off in game textures often leans toward sharpness (gamers prefer crisp textures even if a bit of noise is present, rather than blurry ones). BSRGAN’s bias to sharp (with noise) often wins out for environment textures, whereas RealESRGAN’s clearer approach might be chosen for character faces or things that shouldn’t be noisy. The ideal use cases split accordingly: use BSRGAN for hard surfaces, text, architecture, and very low-res sources where any detail is welcome; use RealESRGAN for organics, faces, or moderately low-res sources where you want detail but not grain; use the anime model for cartoony game art; and use RealESR_Gx4 or denoisers as helpers when you need to keep things stable or clean.

5. Blurry Image Restoration

Blurry images – whether due to motion blur, out-of-focus blur, or low-resolution causing a blurry look – are a challenging scenario. Upscaling alone doesn’t fully solve blur, but these models attempt to sharpen and reconstruct details to counteract it. We consider how they perform when faced with a blurry input that needs clarity:

• RealESRGANx4 is relatively strong on deblurring because of its training on varied degradations (including blur kernels). If you feed a moderately blurry photo (say a slightly out-of-focus portrait or a motion-blurred shot of a pet), RealESRGANx4 will try to sharpen edges and add detail to make the image clearer. It might not perfectly recover text from a motion-blurred sign (some dedicated deblur models or algorithms might do better there), but it often produces a visibly sharper result than the original. The GAN will hallucinate plausible detail: for example, if eyes in a face were blurry, it may invent eyelashes or a sharper eye sparkle that weren’t discernible before. This can be impressive but one must be aware it’s essentially guessing. The artifact risk here is that if the model’s guess is wrong, you get odd textures – e.g., instead of a smooth motion trail it might insert a repeating pattern that looks unnatural. In some cases, very heavy blur (like a totally unfocused image) upscaled by RealESRGAN looks artificially over-sharpened without truly restoring the subject – edges come back but might be misplaced or overshoot, creating a ghosting effect. For mild to moderate blur, though, RealESRGAN significantly improves perceived sharpness.

• BSRGANx4, also trained for blind conditions, does well on unknown blur types. It tends to recover text and fine edges better than many, so if your blurry image has, say, a license plate or small letters, BSRGAN might make them more legible by crisply defining their edges. It also might introduce less “invented” detail than RealESRGAN – instead, it will overlay a grain that can give the impression of detail. For a motion-blurred image, adding a bit of grain and edge contrast (which BSRGAN does) can sometimes trick the eye into seeing it as more detailed than it was. BSRGAN’s risk is that it might not fully remove the blur – you could end up with a somewhat sharpened but still obviously blurred result, just with noise on top. In extreme blur cases, BSRGAN outputs can appear as if a blurry photo was taken on grainy film – i.e., still clearly blurry, but now with texture. This is not necessarily bad; some prefer a little residual blur to weird hallucinations. Use case: if the blur is not too severe and the content has distinct edges that just need definition, BSRGAN will likely succeed. For extremely blurry images (like a face that’s just a smudge), no upscaler will truly fix it, but RealESRGAN might make a more aggressive attempt (with possible bizarre artifacts), whereas BSRGAN might leave it recognizable but not sharp.

• RealESR_Gx4 in blurry restoration will mainly apply a standard sharpening filter effect (by virtue of upscaling) without fancy guesses. A blurry image upscaled with RealESR_Gx4 may look a bit clearer simply because it’s larger and minor sharpening is applied, but it won’t reconstruct fine details that weren’t there. The advantage is it also won’t hallucinate odd textures. If you have a slightly blurry photo that you just want to make a bit more clear and print larger, RealESR_Gx4 could do an acceptable job safely. But if you truly want to restore detail, the GANs are more capable. So consider RealESR_Gx4 a low-risk, moderate-gain tool for blur: low risk of artifacts, but only medium improvement in sharpness. Often one might combine it with a traditional sharpening filter afterward for a small boost, if avoiding GAN artifacts.

• IRCNN_Lx1 specifically might be relevant here: although it’s labeled for denoising, sometimes denoisers help with slight blur by removing low-frequency noise and enhancing contrast. However, typically deblurring is a different task (often involves deconvolution). The IRCNN models are not deconvolution networks; they won’t magically refocus an image. If a blurry image also has noise, using IRCNN to remove the noise can help the upscaler focus on the blur. There are other models (not in our list) like DeblurGAN, etc., but for our list, IRCNN doesn’t directly sharpen blur. It might even smooth edges more (treating a blurred edge as something to average out). So I would say IRCNN tools have Low direct suitability for blur removal. One scenario: if blur comes with jpeg artifacts, denoise those first; the blur itself remains to be handled by the upscaler.

• BSRGANx2 could be used if the blur is mild and you want to upscale gradually. Two passes of mild sharpening might be gentler than one pass of strong. It’s not a huge difference, but sometimes it can avoid overshoot. For example, upscale 2× with BSRGANx2 (image gets a bit sharper), then another 2× possibly with RealESRGAN or BSRGAN again. This staged approach might keep things stable. If RealESRGANx4 in one go produced ringing on a blurred edge, doing BSRGANx2 twice might yield a cleaner edge.

• RealESR_Animex4 is only relevant for blurry anime images (like a low-res anime frame or a smudged animation cel). In those cases, yes, it will restore line sharpness and is absolutely the right choice (as discussed in the anime section). For a blurry cartoon, the anime model will outperform the others because it understands the shapes to reconstruct (e.g., it will make a blurred eye look like a clear drawn eye, whereas a generic model might just sharpen the blur without knowing it should be an outline). So in the context of blurry anime: RealESR_Animex4 – High suitability; others – less so. But for general blurry photographs, ignore the anime model.

Artifacts & Visual Qualities (Blurry Restoration): The battle here is between leaving residual blur vs. introducing false detail. RealESRGAN might remove blur at the cost of some authenticity – you get a sharper but potentially incorrect image (e.g., letters that were unreadable might become readable, but they might actually be different from the true text). BSRGAN might leave a bit more blur but will make what can be sharpened sharper (so you might still not read the letters, but you’ll clearly see the shapes with noise). Oversharpening artifacts like ringing halos are a risk for both but more so for RealESRGAN. BSRGAN’s grain could be considered a mild artifact but also a detail simulator. The sharpness vs naturalness question is tricky for blur: naturalness might mean keeping some blur (since that was the reality of the photo), whereas sharpness means eliminating blur (but at risk of unnatural look). RealESRGAN skews to eliminating blur (sharpness priority), BSRGAN is somewhat in between (sharpens edges, but keeps noise which can mask the fact that the underlying info was missing), RealESR_Gx4 keeps things natural (it won’t introduce weirdness, but your image may remain obviously blurry, just larger). In practice, if the goal is to salvage a blurry image, users often prefer some sharpness even if a bit synthetic – thus RealESRGAN or BSRGAN get used. One might also combine approaches: e.g., use RealESRGAN for a big jump in detail, then if it looks too harsh, blend it slightly with the original or apply slight denoise to tone down artifacts.

Finally, note that none of these models are specialized deblur networks – they do a decent job on mild blur due to being trained on general degradations, but for extremely blurry images, results will be limited. In video, using these on a blurred sequence can lead to a lot of flicker as well (each frame’s blur might get different treatment), so for video deblurring, often specialized methods or temporal models are preferred. However, for a single blurry photo or frame, RealESRGAN and BSRGAN are valuable tools to try.

Comparative Summary Table

Below is a summary comparing the models across content types, typical artifacts, output characteristics, and best use cases: