How does AI Upscaling work?

Short answer: AI upscaling works by training a model on paired low- and high-resolution images, then using it to predict believable extra pixels during upscaling. If the model has seen similar textures or faces in training, it can add convincing detail; if not, it may “hallucinate” artefacts such as halos, waxy skin, or flicker in video. 

Key takeaways:

Prediction: The model generates plausible detail, not a guaranteed reconstruction of reality.

Model choice: CNNs tend to be steadier; GANs can look sharper but risk inventing features.

Artefact checks: Watch for halos, repeated textures, “almost letters”, and plasticky faces.

Video stability: Use temporal methods or you’ll see frame-to-frame shimmer and drift.

High-stakes use: If accuracy matters, disclose processing and treat results as illustrative.

You’ve probably seen it: a tiny, crunchy image turns into something crisp enough to print, stream, or drop into a presentation without wincing. It feels like cheating. And - in the best way - it sort of is 😅

So, How AI Upscaling works comes down to something more specific than “the computer enhances details” (hand-wavy) and closer to “a model predicts plausible high-resolution structure based on patterns it learned from lots of examples” (Deep Learning for Image Super-resolution: A Survey). That prediction step is the whole game - and it’s why AI upscaling can look stunning… or a little plastic… or like your cat grew bonus whiskers.

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How AI Upscaling works: the core idea, in everyday words 🧩

Upscaling means increasing resolution: more pixels, bigger image. Traditional upscaling (like bicubic) basically stretches pixels and smooths transitions (Bicubic interpolation). It’s fine, but it can’t invent new detail - it just interpolates.

AI upscaling tries something bolder (aka “super-resolution” in the research world) (Deep Learning for Image Super-resolution: A Survey):

• It looks at the low-res input

• Recognizes patterns (edges, textures, facial features, text strokes, fabric weave…)

• Predicts what a higher-res version should look like

• Generates extra pixel data that fits those patterns

Not “restore reality perfectly,” more like “make a highly believable guess” (Image Super-Resolution Using Deep Convolutional Networks (SRCNN)). If that sounds slightly suspicious, you’re not wrong - but it’s also why it works so well 😄

And yes, this means AI upscaling is basically controlled hallucination… but in a productive, pixel-respecting way.

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What makes a good version of AI upscaling? ✅🛠️

If you’re judging an AI upscaler (or a setting preset), here’s what tends to matter most:

• Detail recovery without overcooking
  Good upscaling adds crispness and structure, not crunchy noise or fake pores.

• Edge discipline
  Clean lines stay clean. Bad models make edges wobble or sprout halos.

• Texture realism
  Hair shouldn’t become a paintbrush stroke. Brick shouldn’t become a repeating pattern stamp.

• Noise and compression handling
  A lot of everyday images are JPEG’d to death. A good upscaler doesn’t amplify that damage (Real-ESRGAN).

• Face and text awareness
  Faces and text are the easiest places to spot mistakes. Good models treat them gently (or have specialized modes).

• Consistency across frames (for video)
  If detail flickers frame-to-frame, your eyes will scream. Video upscaling lives or dies by temporal stability (BasicVSR (CVPR 2021)).

• Controls that make sense
  You want sliders that map to real outcomes: denoise, deblur, artifact removal, grain retention, sharpening… the practical stuff.

A quiet rule that holds up: the “best” upscaling is often the one you barely notice. It just looks like you had a better camera to begin with 📷✨

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Comparison Table: popular AI upscaling options (and what they’re good for) 📊🙂

Below is a practical comparison. Prices are intentionally fuzzy because tools vary by license, bundles, compute costs, and all that fun stuff.

Tool / Approach	Best for	Price vibe	Why it works (roughly)
Topaz-style desktop upscalers (Topaz Photo, Topaz Video)	Photos, video, easy workflow	Paid-ish	Strong general models + lots of tuning, tends to “just work”… mostly
Adobe “Super Resolution” type features (Adobe Enhance > Super Resolution)	Photographers already in that ecosystem	Subscription-y	Solid detail reconstruction, usually conservative (less drama)
Real-ESRGAN / ESRGAN variants (Real-ESRGAN, ESRGAN)	DIY, developers, batch jobs	Free (but time-costly)	Great at texture detail, can be spicy on faces if you’re not careful
Diffusion-based upscaling modes (SR3)	Creative work, stylized results	Mixed	Can create gorgeous detail - also can invent nonsense, so… yep
Game upscalers (DLSS/FSR-style) (NVIDIA DLSS, AMD FSR 2)	Real-time gaming and rendering	Bundled	Uses motion data and learned priors - smooth performance win 🕹️
Cloud upscaling services	Convenience, quick wins	Pay-per-use	Fast + scalable, but you trade control and sometimes subtlety
Video-focused AI upscalers (BasicVSR, Topaz Video)	Old footage, anime, archives	Paid-ish	Temporal tricks to reduce flicker + specialized video models
“Smart” phone/gallery upscaling	Casual use	Included	Lightweight models tuned for pleasing output, not perfection (still handy)

Formatting quirk confession: “Paid-ish” is doing a lot of work in that table. But you get the idea 😅

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The big secret: models learn a mapping from low-res to high-res 🧠➡️🖼️

At the heart of most AI upscaling is a supervised learning setup (Image Super-Resolution Using Deep Convolutional Networks (SRCNN)):

1. Start with high-resolution images (the “truth”)

2. Downsample them to low-resolution versions (the “input”)

3. Train a model to reconstruct the original high-res from the low-res

Over time, the model learns correlations like:

• “This kind of blur around an eye usually belongs to eyelashes”

• “This pixel cluster often indicates serif text”

• “This edge gradient looks like a rooftop line, not random noise”

It’s not memorizing specific images (in the simple sense), it’s learning statistical structure (Deep Learning for Image Super-resolution: A Survey). Think of it like learning the grammar of textures and edges. Not poetry grammar, more like… IKEA manual grammar 🪑📦 (clunky metaphor, yet close enough).

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The nuts and bolts: what happens during inference (when you upscale) ⚙️✨

When you feed an image into an AI upscaler, there’s typically a pipeline like this:

• Preprocessing

  • Convert color space (sometimes)

  • Normalize pixel values

  • Tile the image into chunks if it’s large (VRAM reality check 😭) (Real-ESRGAN repo (tile options))

• Feature extraction

  • Early layers detect edges, corners, gradients

  • Deeper layers detect patterns: textures, shapes, facial components

• Reconstruction

  • The model generates a higher-res feature map

  • Then converts that into actual pixel output

• Post-processing

  • Optional sharpening

  • Optional denoise

  • Optional artifact suppression (ringing, halos, blockiness)

One subtle detail: many tools upscale in tiles, then blend seams. Great tools hide tile boundaries. Meh tools leave faint grid marks if you squint. And yes, you will squint, because humans love inspecting minute imperfections at 300% zoom like little gremlins 🧌

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The main model families used for AI upscaling (and why they feel different) 🤖📚

1) CNN-based super-resolution (the classic workhorse)

Convolutional neural networks are great at local patterns: edges, textures, small structures (Image Super-Resolution Using Deep Convolutional Networks (SRCNN)).

• Pros: fast-ish, stable, fewer surprises

• Cons: can look a bit “processed” if pushed hard

2) GAN-based upscaling (ESRGAN-style) 🎭

GANs (Generative Adversarial Networks) train a generator to produce high-res images that a discriminator can’t distinguish from real ones (Generative Adversarial Networks).

• Pros: punchy detail, impressive texture

• Cons: can invent detail that wasn’t there - sometimes wrong, sometimes uncanny (SRGAN, ESRGAN)

A GAN can give you that gasp-worthy sharpness. It can also give your portrait subject an extra eyebrow. So… choose your battles 😬

3) Diffusion-based upscaling (the creative wildcard) 🌫️➡️🖼️

Diffusion models denoise step-by-step and can be guided to produce high-res detail (SR3).

• Pros: can be insanely good at plausible detail, especially for creative work

• Cons: can drift away from the original identity/structure if settings are aggressive (SR3)

This is where “upscaling” starts blending into “reimagining.” Sometimes that’s exactly what you want. Sometimes it is not.

4) Video upscaling with temporal consistency 🎞️

Video upscaling often adds motion-aware logic:

• Uses neighboring frames to stabilize detail (BasicVSR (CVPR 2021))

• Tries to avoid flicker and crawling artifacts

• Often combines super-resolution with denoise and deinterlacing (Topaz Video)

If image upscaling is like restoring one painting, video upscaling is like restoring a flipbook without making the character’s nose change shape every page. Which is… harder than it sounds.

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Why AI upscaling sometimes looks fake (and how to spot it) 👀🚩

AI upscaling fails in recognizable ways. Once you learn the patterns, you’ll see them everywhere, like buying a new car and suddenly noticing that model on every street 😵💫

Common tells:

• Wax skin on faces (too much denoise + smoothing)

• Over-sharpened halos around edges (classic “overshoot” territory) (Bicubic interpolation)

• Repeated textures (brick walls become copy-paste patterns)

• Crunchy micro-contrast that screams “algorithm”

• Text mangling where letters become almost-letters (the worst kind)

• Detail drift where small features subtly change, especially in diffusion workflows (SR3)

The tricky part: sometimes these artifacts look “better” at a glance. Your brain likes sharpness. But after a moment, it feels… off.

A decent tactic is to zoom out and check whether it looks natural at normal viewing distance. If it only looks good at 400% zoom, that’s not a win, that’s a hobby 😅

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How AI Upscaling works: the training side, without the math headache 📉🙂

Training super-resolution models usually involves:

• Paired datasets (low-res input, high-res target) (Image Super-Resolution Using Deep Convolutional Networks (SRCNN))

• Loss functions that punish wrong reconstructions (SRGAN)

Typical loss types:

• Pixel loss (L1/L2)
  Encourages accuracy. Can produce slightly soft results.

• Perceptual loss
  Compares deeper features (like “does this look similar”) rather than exact pixels (Perceptual Losses (Johnson et al., 2016)).

• Adversarial loss (GAN)
  Encourages realism, sometimes at the cost of literal accuracy (SRGAN, Generative Adversarial Networks).

There’s a constant tug-of-war:

• Make it faithful to the original
  vs

• Make it visually pleasing

Different tools land in different places on that spectrum. And you might prefer one depending on whether you’re restoring family photos or prepping a poster where “good-looking” matters more than forensic accuracy.

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Practical workflows: photos, old scans, anime, and video 📸🧾🎥

Photos (portraits, landscapes, product shots)

Best practice is usually:

• Mild denoise first (if needed)

• Upscale with conservative settings

• Add grain back if things feel too smooth (yes, really)

Grain is like salt. Too much ruins dinner, but none at all can taste a bit flat 🍟

Old scans and heavily compressed images

These are harder because the model might treat compression blocks as “texture.”
Try:

• Artifact removal or deblocking

• Then upscale

• Then light sharpening (not too much… I know, everyone says that, but still)

Anime and line art

Line art benefits from:

• Models that preserve clean edges

• Reduced texture hallucination
  Anime upscaling often looks great because the shapes are simpler and consistent. (Lucky.)

Video

Video adds extra steps:

• Denoise

• Deinterlace (for certain sources)

• Upscale

• Temporal smoothing or stabilization (BasicVSR (CVPR 2021))

• Optional grain reintroduction for cohesion

If you skip temporal consistency, you get that shimmering detail flicker. Once you notice it, you can’t unsee it. Like a squeaky chair in a quiet room 😖

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Picking settings without guessing wildly (a small cheat sheet) 🎛️😵💫

Here’s a decent starting mindset:

• If faces look plasticky
  Reduce denoise, reduce sharpening, try a face-preserving model or mode.

• If textures look too intense
  Lower “detail enhancement” or “recover detail” sliders, add subtle grain after.

• If edges glow
  Turn down sharpening, check halo suppression options.

• If the image looks too “AI”
  Go more conservative. Sometimes the best move is simply… less.

Also: don’t upscale 8x just because you can. A clean 2x or 4x is often the sweet spot. Past that, you’re asking the model to write fanfiction about your pixels 📖😂

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Ethics, authenticity, and the awkward question of “truth” 🧭😬

AI upscaling blurs a line:

• Restoration implies recovering what was there

• Enhancement implies adding what wasn’t

With personal photos, it’s usually fine (and lovely). With journalism, legal evidence, medical imaging, or anything where fidelity matters… you need to be careful (OSAC/NIST: Standard Guide for Forensic Digital Image Management, SWGDE Guidelines for Forensic Image Analysis).

A simple rule:

• If the stakes are high, treat AI upscaling as illustrative, not definitive.

Also, disclosure matters in professional contexts. Not because AI is evil, but because audiences deserve to know whether details were reconstructed or captured. That’s just… respectful.

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Closing notes and a quick recap 🧡✅

So, How AI Upscaling works is this: models learn how high-resolution detail tends to relate to low-resolution patterns, then predict believable extra pixels during upscaling (Deep Learning for Image Super-resolution: A Survey). Depending on the model family (CNN, GAN, diffusion, video-temporal), that prediction can be conservative and faithful… or bold and at times unhinged 😅

Quick recap

• Traditional upscaling stretches pixels (Bicubic interpolation)

• AI upscaling predicts missing detail using learned patterns (Image Super-Resolution Using Deep Convolutional Networks (SRCNN))

• Great results come from the right model + restraint

• Watch for halos, waxy faces, repeated textures, and flicker in video (BasicVSR (CVPR 2021))

• Upscaling is often “plausible reconstruction,” not perfect truth (SRGAN, ESRGAN)

If you want, tell me what you’re upscaling (faces, old photos, video, anime, text scans), and I’ll suggest a settings strategy that tends to dodge the common “AI look” pitfalls 🎯🙂

Real-world example: Upscaling old marketplace product photos 📸

Scenario

A small second-hand camera shop has 40 product photos exported from an old website at 800px wide. The owner wants to reuse them on a new ecommerce page, where the recommended image size is 1,600px wide.

The problem: normal resizing makes the cameras look soft, while aggressive AI upscaling can make rubber grips, serial numbers, and lens markings look suspiciously fake. That matters because buyers rely on those details before purchasing.

The goal is not to “restore” missing information perfectly. It is to create cleaner listing images while keeping the original files available, because AI upscaling predicts plausible detail rather than guaranteed truth.

What the workflow needs

Original product photos, ideally the least compressed versions available

A target output size, such as a 2× upscale from 800px to 1,600px wide

A tool or model with separate controls for denoise, sharpening, and artefact removal

A simple review checklist for text, edges, logos, screws, buttons, leather grain, and reflections

A folder for originals and a separate folder for edited exports, so nothing gets overwritten

Example instruction

Use this kind of instruction when testing an AI upscaler:

Upscale this product photo by 2× for an ecommerce listing. Keep the object shape, logo placement, lens markings, button edges, and surface texture as close to the original as possible. Use mild compression clean-up, low sharpening, and avoid inventing extra text, scratches, labels, serial numbers, or decorative detail. The final image should look natural at normal product-page size, not artificially sharp at 400% zoom.

How to test it

Start with five mixed images before processing the full batch:

One clean product photo with good lighting

One JPEG-compressed image with blockiness

One photo with tiny printed text or lens markings

One dark image with noise in the shadows

One image with reflective metal or glass

After upscaling, compare each result against the original at 100% and 200%. Check whether brand names, dials, screws, ports, and texture patterns still match. If the model creates “almost letters” or fake surface marks, lower the sharpening or detail recovery setting.

Result

Illustrative result: based on timing a five-image test before and after using this workflow.

Manual clean-up and resizing took about 9 minutes per image, or 45 minutes for five images.

The AI-assisted workflow took about 3 minutes per image, or 15 minutes for five images.

That is an estimated 30 minutes saved on five images, or around 4 hours saved across a 40-image batch.

Quality check result: 4 out of 5 images passed the first review. One image failed because the upscaler distorted small lens text, so it was reprocessed with lower sharpening and no text enhancement.

The valuable metric here is not just “looks sharper”. It is: how many images pass a side-by-side review without invented details?

What can go wrong

The model may turn dust, JPEG blocks, or scratches into “real” texture.

Tiny text can become fake text that looks believable until you zoom in.

Too much denoise can make rubber, leather, or brushed metal look waxy.

Strong sharpening can create halos around product edges.

Batch processing can hide mistakes, so review a sample before exporting everything.

For ecommerce, the safest rule is simple: never use AI upscaling to hide damage, change condition, or make a product look newer than it is.

Practical takeaway

AI upscaling works best when you treat it as a controlled finishing step, not a magic repair button. Use conservative 2× settings, check the details buyers care about, and keep the original image so the edited version stays credible.

Real-world example: Upscaling an old training video without making it shimmer

Scenario

A small training company has a 7-minute safety demonstration video recorded in 2014 at 720p. The content still has value, but the footage looks soft on the company’s new website, especially on larger laptop screens.

The team wants to export a cleaner 1080p version without reshooting. The risk is that aggressive AI upscaling could make faces look waxy, turn text on signs into “almost words”, or create flickering texture from frame to frame.

The goal is not to make the video look brand new. It is to make it clearer, steadier, and less compressed while keeping the instructor’s face, warning labels, hand movements, and equipment details faithful to the original.

What the workflow needs

Original video file, not a compressed social media download if possible

Target export size, such as 720p to 1080p rather than jumping straight to 4K

A video upscaler with denoise, sharpening, compression repair, and temporal consistency options

A short test clip with faces, movement, text, and detailed surfaces

A review checklist for flicker, halos, warped text, face texture, and moving edges

A saved copy of the original video for comparison and disclosure if needed

Example instruction

Use this kind of instruction before processing the full video:

Upscale this 720p training video to 1080p. Prioritise natural motion, stable edges, readable existing text, and realistic skin texture. Use mild compression repair and low sharpening. Do not invent missing text, logos, labels, scratches, facial detail, or equipment markings. Avoid frame-to-frame shimmer. The final result should look clearer at normal viewing size, not artificially sharp when paused and zoomed in.

How to test it

Before processing the full 7-minute file, export a 20-second sample that includes:

The instructor’s face while speaking

A hand moving across the frame

A warning label or small printed text

A textured surface, such as fabric, concrete, brushed metal, or plastic

A camera pan or any shaky movement

Watch the sample twice: once at normal speed and once paused frame by frame. At normal speed, look for flicker, crawling texture, or unnatural motion around edges. When paused, compare the original and upscaled versions to check whether text, buttons, tools, and facial features still match.

Result

Illustrative result: based on timing one 20-second test clip and then applying the same settings to a 7-minute video.

A manual “resize and sharpen” workflow took about 35 minutes, including export and review, but the result showed visible shimmer on the instructor’s hair and halos around safety signs.

The AI-assisted workflow took about 55 minutes including test exports, but reduced review problems from 8 visible issues in the first export to 2 minor issues in the final export.

The final version passed 10 out of 12 checks on the review checklist. The two remaining issues were slight softness on background text and mild noise in one dark corner. Both were accepted because the instructor, equipment, and safety steps stayed visually consistent.

The meaningful metric here is not “1080p achieved”. It is: how many seconds of the video show distracting artefacts during normal playback?

What can go wrong

The model may sharpen compression blocks and make them look like genuine texture.

Fine text can become more confident-looking but less accurate.

Faces can become too smooth if denoise is too high.

Moving edges can shimmer if the tool treats each frame too independently.

A 4K export can look worse than a restrained 1080p export because the model has to invent too much detail.

The biggest mistake is judging only a paused frame. Video upscaling has to look natural in motion, not just impressive as a still image.

Practical takeaway

For video, AI upscaling works best when you test a short section first, keep the upscale modest, and judge motion before sharpness. A slightly softer but stable result is usually better than a crisp version that flickers every time someone moves.

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FAQ

AI upscaling and how it works

AI upscaling (often called “super-resolution”) increases an image’s resolution by predicting missing high-resolution detail from patterns learned during training. Instead of simply stretching pixels like bicubic interpolation, a model studies edges, textures, faces, and text-like strokes, then generates new pixel data that coheres with those learned patterns. It’s less “restoring reality” and more “making a believable guess” that reads as natural.

AI upscaling versus bicubic or traditional resizing

Traditional upscaling methods (like bicubic) mainly interpolate between existing pixels, smoothing transitions without creating true new detail. AI upscaling aims to reconstruct plausible structure by recognizing visual cues and predicting what high-res versions of those cues tend to look like. That’s why AI results can feel dramatically sharper, and also why they can introduce artifacts or “invent” details that weren’t present in the source.

Why faces can look waxy or overly smooth

Waxy faces usually come from aggressive denoising and smoothing paired with sharpening that strips away natural skin texture. Many tools treat noise and fine texture similarly, so “cleaning” an image can erase pores and subtle detail. A common approach is to reduce denoise and sharpening, use a face-preserving mode if available, then reintroduce a touch of grain so the result feels less plastic and more photographic.

Common AI upscaling artifacts to watch for

Typical tells include halos around edges, repeated texture patterns (like copy-paste bricks), crunchy micro-contrast, and text that turns into “almost letters.” In diffusion-based workflows, you can also see detail drift where small features subtly change. For video, flicker and crawling detail across frames are big red flags. If it only looks good at extreme zoom, the settings are probably too aggressive.

How GAN, CNN, and diffusion upscalers tend to differ in results

CNN-based super-resolution tends to be steadier and more predictable, but it can look “processed” if pushed hard. GAN-based options (ESRGAN-style) often produce punchier texture and perceived sharpness, but they can hallucinate incorrect detail, especially on faces. Diffusion-based upscaling can generate beautiful, plausible detail, yet it may drift from the original structure if the guidance or strength settings are too strong.

A practical settings strategy for avoiding a “too AI” look

Start conservative: upscale 2× or 4× before reaching for extreme factors. If faces look plasticky, dial back denoise and sharpening and try a face-aware mode. If textures get too intense, lower detail enhancement and consider adding subtle grain afterward. If edges glow, reduce sharpening and check halo or artifact suppression. In many pipelines, “less” wins because it preserves believable realism.

Handling old scans or heavily JPEG-compressed images before upscaling

Compressed images are tricky because models can treat block artifacts as real texture and amplify them. A common workflow is artifact removal or deblocking first, then upscaling, then light sharpening only if needed. For scans, gentle cleanup can help the model focus on actual structure rather than damage. The goal is to reduce “fake texture cues” so the upscaler isn’t forced to make confident guesses from noisy inputs.

Why video upscaling is harder than photo upscaling

Video upscaling has to be consistent across frames, not just good on one still image. If details flicker frame-to-frame, the result becomes distracting fast. Video-focused approaches use temporal information from neighboring frames to stabilize reconstruction and avoid shimmering artifacts. Many workflows also include denoise, deinterlacing for certain sources, and optional grain reintroduction so the whole sequence feels cohesive rather than artificially sharp.

When AI upscaling is not appropriate or is risky to rely on

AI upscaling is best treated as enhancement, not proof. In high-stakes contexts like journalism, legal evidence, medical imaging, or forensic work, generating “believable” pixels can mislead because it may add details that weren’t captured. A safer framing is to use it illustratively and disclose that an AI process reconstructed detail. If fidelity is critical, preserve originals and document every processing step and setting.

References

1. arXiv - Deep Learning for Image Super-resolution: A Survey - arxiv.org

2. arXiv - Image Super-Resolution Using Deep Convolutional Networks (SRCNN) - arxiv.org

3. arXiv - Real-ESRGAN - arxiv.org

4. arXiv - ESRGAN - arxiv.org

5. arXiv - SR3 - arxiv.org

6. NVIDIA Developer - NVIDIA DLSS - developer.nvidia.com

7. AMD GPUOpen - FidelityFX Super Resolution 2 - gpuopen.com

8. The Computer Vision Foundation (CVF) Open Access - BasicVSR: The Search for Essential Components in Video Super-Resolution (CVPR 2021) - openaccess.thecvf.com

9. arXiv - Generative Adversarial Networks - arxiv.org

10. arXiv - SRGAN - arxiv.org

11. arXiv - Perceptual Losses (Johnson et al., 2016) - arxiv.org

12. GitHub - Real-ESRGAN repo (tile options) - github.com

13. Wikipedia - Bicubic interpolation - wikipedia.org

14. Topaz Labs - Topaz Photo - topazlabs.com

15. Topaz Labs - Topaz Video - topazlabs.com

16. Adobe Help Centre - Adobe Enhance > Super Resolution - helpx.adobe.com

17. NIST / OSAC - Standard Guide for Forensic Digital Image Management (Version 1.0) - nist.gov

18. SWGDE - Guidelines for Forensic Image Analysis - swgde.org

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