I remember the exact moment I needed an image upscaler. I was putting together a banner for a client presentation, and the only logo they had was a 200x200 pixel JPEG that had been emailed around the office for six years. It looked fine when you viewed it at 100 percent on a phone screen, but when I blew it up to 1200 pixels wide for a slide deck, it was a pixelated disaster. The edges were jagged, the text was unreadable, and there was no way I could present it to the client. I spent the next two hours trying every trick I knew to salvage that logo, and nothing worked.

That was two years ago, before AI-powered upscaling became widely accessible to everyone. Back then my options were limited and all of them were bad. Photoshop's Preserve Details 2.0 enlargement algorithm was the best built-in option, but at anything beyond 2x magnification it turned fine details into a soft watercolor mess that looked more like an impressionist painting than a professional logo. Third-party plugins like ON1 Resize and Topaz Gigapixel were effective but expensive, costing anywhere from $50 to $200 for a license. And the free options like nearest-neighbor interpolation turned smooth curves into jagged staircases that looked like something from an 8-bit video game. None of these were acceptable solutions for professional client work.

Today the landscape has completely changed. AI-powered image upscaling has matured to the point where you can take a 72 DPI web graphic and enlarge it to 300 DPI print resolution without introducing blur, artifacts, or jagged edges. You can take a grainy smartphone photo from 2014 shot on a 5-megapixel camera and make it look like it was captured on modern flagship equipment. You can rescue old scanned family photos from the 1970s and make them sharp enough to print, frame, and hang on your wall. And you can do all of this online completely for free, without installing a single piece of software or creating an account.

In this comprehensive guide I will share everything I have learned about AI image upscaling over the past two years of working with these tools on a daily basis for both personal projects and client work. I will explain exactly how the technology works under the hood so you know why it produces such remarkable results. I will walk you through when you should use AI upscaling and when you should not, because knowing the limitations is just as important as knowing the capabilities. I will give you my exact step-by-step workflow that consistently delivers professional-grade results. And I will show you how to avoid the most common pitfalls that trip up beginners and can ruin an otherwise perfect upscale.

What Is AI Image Upscaling and How Does It Actually Work?

To understand why AI upscaling represents such a dramatic leap forward compared to everything that came before, you first need to understand how traditional image enlargement works and why it falls short. When you open an image in Photoshop or any other editing software and increase its pixel dimensions, the software is faced with a fundamental problem: it needs to create brand new pixels that never existed in the original file. Traditional enlargement algorithms like bilinear and bicubic interpolation handle this by examining the existing neighboring pixels and performing mathematical averaging to estimate what color the new pixels should be. If you have a white pixel next to a black pixel and you double the resolution, the interpolation algorithm creates a gray pixel in between. This approach works reasonably well at small scaling factors like 1.1x or 1.2x, but at larger factors like 2x or 4x the result becomes increasingly soft, blurry, and lacking in detail because the algorithm is essentially just making educated guesses based on simple math.

AI upscaling approaches the problem from a completely different direction. Instead of blindly averaging neighboring pixels, the AI model has been trained on an enormous dataset of millions of high-resolution images spanning every conceivable subject and category. The training set includes portraits, landscapes, product shots, architectural photography, text and documents, animals, food, macro photography, and hundreds of other categories. During training the model learns to recognize patterns and textures at multiple scales. When it receives a low-resolution input image it does not simply enlarge it. It actively analyzes the content and recognizes the patterns it has seen during training. It identifies the texture of human skin versus the texture of fabric. It distinguishes between the sharp edge of a building against the sky and the soft blur of a distant mountain. It recognizes the fine individual strands of hair in a portrait and the weave pattern of fabric in a clothing photo.

Once the AI has identified these patterns, it uses that knowledge to reconstruct the missing detail at higher resolution. It effectively paints in the missing information based on what it has learned real high-resolution textures and details should look like. This is why AI upscaling can add detail that was never present in the original image. The AI is not guessing blindly. It is applying learned knowledge about how the world looks to fill in the gaps intelligently. The result is an upscaled image that can look remarkably close to a native high-resolution capture, with sharp edges, natural textures, and fine details preserved in a way that traditional interpolation methods simply cannot achieve.

ℹ

AI vs Traditional Upscaling: Traditional interpolation methods like bicubic averaging add blurry guesses based on simple mathematical averaging of neighboring pixels. AI upscaling adds intelligent, context-aware detail based on patterns and textures learned from millions of real high-resolution images during training. The difference is most visible on challenging content like faces with fine features, text with sharp edges, and complex natural textures like grass, leaves, fabric, or hair where traditional methods produce mushy, indistinct results while AI maintains crisp, natural-looking detail.

The underlying technology powering most modern AI upscalers is a type of deep learning architecture called a Super-Resolution Generative Adversarial Network, or SRGAN for short. This sophisticated system actually uses two separate neural networks that work together in a competitive fashion. The generator network is responsible for creating the upscaled image based on the low-resolution input. The discriminator network tries to distinguish between the AI-generated upscaled image and a genuine high-resolution image of the same content. Through millions of iterations of this adversarial training process, the generator becomes increasingly skilled at producing results that can fool the discriminator, which means they look increasingly realistic and natural to the human eye.

The newest generation of AI upscalers has moved beyond SRGAN architectures to use diffusion models, which are the same type of AI technology that powers popular tools like Midjourney, DALL-E, and Stable Diffusion for generating images from text descriptions. Diffusion models approach the upscaling task from a fundamentally different angle. Instead of trying to generate new detail from scratch, they treat the low-resolution image as a degraded version of a high-resolution image that has had noise and detail progressively removed. The diffusion model then reverses this process, gradually removing the degradation and restoring the original high-resolution detail. This approach tends to produce even more natural-looking results with fewer artifacts, particularly on challenging subjects like human faces and complex natural textures.

When Should You Use AI Image Upscaling?

Through extensive testing across hundreds of images I have found that AI upscaling works beautifully in some scenarios and produces disappointing or even unusable results in others. Understanding when to use it and when to avoid it is essential for getting good results. Here is my detailed breakdown of the best use cases based on real testing.

Product Photos for E-Commerce and Online Stores

Low-resolution product images from suppliers or manufacturers are one of the most common use cases for AI upscaling in my professional work. If you run an e-commerce store, you have probably encountered the problem of receiving product photos from your suppliers that are 800x800 pixels or smaller. Major platforms like Amazon require at least 1000x1000 pixels for zoom functionality, and 2000x2000 is recommended for best results. Reshooting all your products is expensive and time-consuming. AI upscaling solves this problem perfectly. I regularly upscale product images from 800x800 to 3200x3200 pixels for my e-commerce clients, and the results are consistently indistinguishable from native high-resolution product photography.

Why does product photography upscale so well? The clean studio lighting, high contrast between the product and background, and the relatively simple composition all give the AI excellent source material to work with. The product is typically well-lit with even illumination, there is minimal visual noise, and the subject is clearly defined against a simple background. These conditions allow the AI to focus its reconstruction capabilities on the product itself rather than wasting processing power trying to make sense of complex backgrounds or poor lighting.

Old Family Photos and Historical Images

This is perhaps the most emotionally rewarding use case for AI upscaling. Scanned family photos from the 1990s and early 2000s were typically scanned at 150 to 300 DPI, which for a standard 4x6 inch print produces images that are only 600x900 to 1200x1800 pixels. On modern high-resolution displays these images look small and pixelated. AI upscaling can more than double their resolution while adding back detail that was lost to the limitations of older scanning technology and lower-resolution prints.

I upscaled a collection of my grandparents' wedding photos from 1952 and the results were genuinely emotional for my entire family. Faces that were barely recognizable smudges of pixels became clear and distinct. Fine details in the wedding dress that had been lost to decades of print degradation were reconstructed. The photos went from being nostalgic artifacts that we could barely see to vibrant images that we could print at 8x10 inches and frame. If you have old family photos that look small and blurry on modern screens, AI upscaling can transform them in ways that would have seemed like magic just a few years ago.

Social Media Graphics and Marketing Materials

How many times have you found the perfect image for a social media post or marketing campaign only to realize the file is too small for the platform's recommended dimensions? This happens constantly in my line of work. A client sends a logo that is 200 pixels wide but the website banner needs to be 1920 pixels wide. A stock photo is perfect but only comes in a small preview size. A screenshot of a product feature needs to be enlarged for a tutorial but goes blurry with traditional methods. AI upscaling solves all of these problems effortlessly.

The key advantage of AI upscaling for marketing materials is that it preserves text readability and sharp edges. Traditional interpolation turns text into blurry, unreadable smudges. AI upscaling keeps text crisp and legible, which is essential for any marketing image that contains headlines, captions, or calls to action. This makes it possible to repurpose small graphics across multiple platforms and formats without losing quality or having to recreate them from scratch.

Screenshots and UI Mockups for Documentation

If you are a developer, designer, or technical writer, you have probably needed to enlarge screenshots or UI mockups for documentation, presentations, or portfolio pieces. The problem is that screenshots are typically captured at screen resolution, and if you need to display them larger than their native size, they become visibly pixelated. AI upscaling preserves the sharp edges of UI elements, buttons, text, and icons in a way that traditional interpolation methods simply cannot match.

I use AI upscaling regularly for taking mobile app screenshots captured at 375x812 pixels and enlarging them to fill a 1920x1080 presentation slide. The result looks crisp and professional, with all UI text remaining readable and all interface elements maintaining their sharp edges. Without AI upscaling, those enlarged screenshots would be a blurry mess that would undermine the professionalism of the presentation.

Print Materials and Large Format Output

Print production requires images at 300 DPI resolution. If your digital image is only 72 DPI at the size you want to print, you need to enlarge it by roughly 4x to achieve print-quality resolution. A standard web-resolution image of 800x600 pixels at 72 DPI would print at only about 2.67x2 inches at acceptable quality. With AI upscaling to 3200x2400 pixels, that same image can print at 10.67x8 inches at 300 DPI - large enough for a framed print or a poster.

The real magic happens when you compare the AI-upscaled print with a traditionally enlarged print. The AI version will have sharp edges, natural textures, and no visible artifacts. The traditionally enlarged version will be soft, blurry, and look obviously artificial. For any print work where image quality matters, AI upscaling is now an essential step in the workflow.

⚠

What AI Upscaling Absolutely Cannot Do: It cannot fix images that are fundamentally out of focus or blurred by camera motion. If the original image is blurry because the lens was not properly focused or because the camera moved during exposure, adding more pixels with AI will simply give you a larger blurry image. The AI does not have magical powers to recover detail that was never captured in the first place. It also struggles significantly with faces at extremely low resolutions below approximately 100x100 pixels. At that size there is simply not enough information in the original pixels for the AI to reconstruct believable facial features. The result often looks like an approximation of a face that falls into the uncanny valley - vaguely human but clearly not a real person.

The Complete Step-by-Step Workflow I Use for AI Image Upscaling

After upscaling hundreds of images professionally I have developed and refined a precise workflow that consistently delivers excellent results across diverse image types and use cases. Here is my complete process broken down into steps.

Start With the Absolute Best Source Image You Can Get Your Hands On

The most important factor in determining the quality of your final upscaled result is the quality of your source image. Before you touch any upscaling tool, make absolutely sure you are working with the highest-resolution version of the image that is available to you. If you have a choice between a 300x300 pixel version and a 600x600 pixel version of the same image, there is no contest - use the larger one every single time. The AI model will have significantly more pixel data to analyze and learn from, and the final result will be noticeably more accurate and detailed. Also check that your source image is not compressed with heavy JPEG artifacts at quality settings below 60 percent. Those blocky 8x8 pixel compression artifacts will be amplified during upscaling and will be much more visible in the final output.

Select the Appropriate Upscaling Factor for Your Specific Use Case

Most AI upscaling tools support magnification factors of 2x, 4x, and sometimes 8x relative to the original dimensions. Through extensive testing I have developed clear guidelines for which factor to use in different situations. Use 2x for images that will primarily be viewed on screens and monitors. This factor produces the most natural-looking results with no visible artifacts because the AI only needs to create a modest amount of new information. Use 4x for images that will be printed or displayed on large high-resolution screens, as this factor provides enough pixels for high-quality output. Only use 8x for extreme cases where the original image is very small and you have absolutely no other option available to you. Going beyond 4x in a single pass frequently introduces visible artifacts because the AI has to invent too much new information at once. If you need 8x enlargement, I strongly recommend doing it in two separate passes. Upscale first at 2x, save the intermediate result, then upscale that result at 4x. The two-pass approach gives the AI better intermediate data to work with in the second pass.

Choose the Right AI Model or Processing Mode for Your Image Type

Many advanced AI upscalers offer multiple different AI models or processing modes that are optimized for different types of images. Choosing the wrong mode can produce noticeably worse results, so this is an important decision. A standard general-purpose or photo model works well for most ordinary photographs including portraits, landscapes, and everyday snapshots. A dedicated text or graphics mode is specifically optimized for screenshots, UI mockups, infographics, and images containing text because it is designed to preserve sharp edges and prevent the ringing artifacts that commonly appear around high-contrast text characters when processed with a general-purpose model. Some tools also offer a specialized face enhancement or portrait mode that applies additional optimization specifically for facial features including eyes, skin texture, and hair detail. Select the mode that matches your image type for the best possible outcome.

Upload Your Image and Let the AI Do Its Processing Work

Upload your source image to the AI upscaler tool of your choice. Most tools support the common web image formats including JPEG, PNG, WebP, and many also accept TIFF and BMP. Some tools even handle camera RAW formats for maximum flexibility. The processing time varies significantly depending on several factors. The raw dimensions of your source image matter. A small 500x500 pixel image will process much faster than a 2000x2000 pixel image. The upscaling factor matters enormously. A 2x upscale of a standard 1 MB image typically completes in 5 to 15 seconds, while a 4x upscale of a larger 5 MB image may take 30 to 60 seconds or more depending on the complexity of the AI model and the current server load. For batch processing of multiple images, plan for longer total processing times and check whether your chosen tool supports queuing for efficient batch workflows.

Carefully Compare the Upscaled Result With the Original Image at Full Magnification

After the AI has finished processing, most quality upscalers provide a side-by-side comparison view or a draggable before-and-after slider that lets you directly compare the original with the upscaled result. Always take the time to use this feature thoroughly. Zoom in to 100 percent magnification and methodically examine several critical areas of the image. Check edges to make sure they are sharp and clean without jagged stair-stepping. Check text to confirm it remains crisp and readable. Examine facial features to verify they look natural and recognizable. Look at fine textures like hair, fabric, or grass to ensure they have detail rather than being smoothed into a mushy blur. The AI should have added visible sharpness and detail without introducing any obviously artificial patterns, weird textures, or plastic-looking surfaces. If you notice any issues, try reducing the upscaling factor or switching to a different AI model for better results.

Download Your Upscaled Image in the Most Appropriate File Format for Your Needs

Save your final upscaled image in a file format that is appropriate for its intended use to avoid unnecessary quality loss. For web use and social media sharing, save as JPEG at a quality setting of 85 to 90 percent. This provides an excellent balance of visual quality and file size. If your image contains text, graphics, or requires transparency like a logo or icon, save as PNG to preserve crisp edges and the transparent background. For print production or archival purposes, save as PNG or TIFF to maintain the maximum possible quality without any compression artifacts whatsoever. Never repeatedly save and recompress a JPEG file, as each generation of JPEG compression discards additional quality. If you might need both a high-quality archival version and a smaller web-optimized version, save the lossless PNG or TIFF first and then create a compressed JPEG copy for web use from that archival master.

Apply Final Touch-Ups in an Image Editor if the AI Introduced Minor Imperfections

While modern AI upscalers produce excellent results out of the box, occasionally the AI may introduce very minor artifacts around high-contrast edges or in areas with extremely complex textures. A quick one-minute pass with any basic image editor can perfect the result. Use a subtle unsharp mask or smart sharpen filter with a low radius and modest amount to enhance edge definition if the result looks slightly soft. Use a healing brush or clone stamp tool to remove any small blemishes or artifacts the AI may have generated in uniform areas like a blue sky or a plain wall. This optional touch-up step is not necessary for most images, but if you are producing work for a client who demands pixel-perfect quality or for large-format print where every imperfection will be visible, it is well worth the extra minute or two of effort.

How Different Image Types Respond to AI Upscaling

Not all images respond to AI upscaling in the same way. Based on my extensive testing across hundreds of images I can provide the following guidance for different image categories.

Portrait and People Photography

AI upscalers generally excel at processing faces and people because facial recognition and reconstruction are among the most heavily trained and refined capabilities in modern computer vision systems. The training datasets for these models contain millions of diverse faces from all over the world, representing every age group, ethnicity, and facial structure. This extensive training means the models know how to reconstruct skin texture, eyes, lips, individual strands of hair, and subtle facial expressions with remarkable accuracy. For the best portrait results, ensure the face occupies at least 150 pixels in height in the original source image. Below this threshold the AI has insufficient information to work with and results degrade rapidly. Also pay close attention to skin tone consistency after upscaling. Some AI models have a known tendency to subtly alter or shift skin tones, so compare the upscaled result carefully with the original color reference.

Text, Documents, and Screenshots

Text is one of the most challenging subjects for AI upscalers because the extremely sharp, high-contrast edges between text characters and their background can produce distracting ringing artifacts. These appear as subtle ghost echoes or halos around the text, particularly at higher upscaling factors. This is precisely why choosing the correct AI model matters so much for text-heavy images. A dedicated text or graphics processing mode applies different edge-handling rules that minimize ringing while preserving the sharpness of character edges. If your image contains both text and photographic elements, you may need to experiment with different modes to achieve the best balance between text clarity and image quality.

For pure text documents like scanned contracts or certificates, consider whether you actually need to upscale the image at all. Running the document through optical character recognition software and then reformatting the text in a word processor or PDF editor is often a better approach that produces searchable, selectable text instead of a large image. Reserve AI upscaling for text-plus-image situations like screenshots, infographics, and social media graphics.

Landscape and Nature Photography

Complex natural textures including grass, leaves, tree bark, water ripples, and rock formations can be surprisingly challenging for AI upscalers. Lower-quality or older models tend to smooth these intricate textures into a mushy, painterly, or watercolor-like appearance that looks artificial and unappealing. The best modern AI models, particularly those using diffusion-based architectures, have improved dramatically at handling natural textures by preserving the fine detail structure rather than smoothing it into a generic texture. If your landscape photo looks like an oil painting after upscaling, try a different tool or reduce the upscaling factor to 2x instead of 4x.

Architecture and Urban Photography

Buildings, cityscapes, and architectural photography consistently produce some of the best AI upscaling results because they contain clean geometric lines, repeating patterns like windows and bricks, and high contrast between structural elements and the sky or background. The AI excels at recognizing and reconstructing these patterns with high accuracy. Pay careful attention to straight lines after upscaling. If the vertical lines of buildings appear wavy, bowed, or distorted, the AI tool you are using is not handling geometric structures properly and you should try a different tool that specifically handles architectural content better.

Save your upscaled image in a format appropriate for its intended use to avoid unnecessary quality loss. For web and social media, save as JPEG at quality 85-90. For images with text or transparency, use PNG. For print and archival purposes, use PNG or TIFF. Never repeatedly save and recompress JPEG files as each generation loses quality. If you need both archival and web versions, save the lossless version first then create a compressed copy for web use.

Common AI Upscaling Mistakes and Practical Solutions

After two years of regular use I have made just about every possible mistake with AI upscaling so you do not have to. The most common error is trying to upscale from a poor-quality source image. The principle of garbage in garbage out applies very directly here. If your source image is heavily compressed with visible JPEG artifacts, significantly out of focus, or has excessive digital noise from high ISO settings, the AI will amplify all of these flaws along with the image detail. Always use the highest quality source image you can obtain.

Another frequent mistake is using an unnecessarily high upscaling factor. Just because a tool advertises 8x or 16x upscaling does not mean you should use it. Higher factors force the AI to create more new information, increasing the probability of visible artifacts and unnatural textures. Stick with 2x for screen viewing and 4x for print, and only push higher when absolutely necessary. Always use the before-and-after comparison feature before downloading. What looks good in a small preview window may reveal obvious artifacts when viewed at full magnification. Finally, never overwrite your original source file with the upscaled version. Keep the original safely backed up in case you need to reprocess it with different settings or a better tool in the future.

Privacy and Security Considerations When Using Online Upscalers

When you upload an image to an online AI upscaling service, the image data must be transmitted over the internet to a remote server where the AI processing actually happens. This raises legitimate privacy concerns that you should consider before uploading sensitive images. If you are working with confidential client work, personal family photos you do not want shared, medical images, or any content covered by a non-disclosure agreement, you should take privacy implications seriously.

ℹ

Privacy Best Practices for Online AI Upscaling: Only use tools that transmit your images over HTTPS encrypted connections - look for the padlock icon in your browser address bar. Verify that images are automatically and permanently deleted from the processing servers immediately after your download is complete. Read the privacy policy carefully to confirm that uploaded images are not used for training or improving the AI model. Avoid tools that require you to create an account, as accounts typically link your uploads to your personal identity and may store a history of your uploads. For the absolute highest level of privacy, look for tools that offer client-side processing where the AI runs locally in your browser or on your own device.

Frequently Asked Questions About AI Image Upscaling

Is AI image upscaling really free to use?

Many online AI upscaling tools offer genuinely free service tiers with reasonable limitations on file size and the number of images you can process per day. Free tiers typically support up to 4x magnification and files up to approximately 5 to 10 MB in size. Some tools require payment for very high upscaling factors, priority processing when servers are busy, or batch processing of more than a few images at once. For occasional personal use, free tools are more than sufficient. For regular professional use, consider a paid subscription for the higher limits and priority processing.

What is the maximum size I can upscale an image to?

Most online AI upscalers support up to 4x magnification in a single processing pass. If you start with a 500x500 pixel image, 4x upscaling will produce a 2000x2000 pixel result. Some advanced tools offer 8x or even 16x upscaling, but quality degrades noticeably at these extreme factors because the AI must invent too much new information. For the best balance of quality and size increase, stay at 4x or below. If you need more than 4x, use the two-pass approach of 2x followed by 4x for better results than a single 8x pass.

Can I upscale images directly on my phone?

Yes, most online AI upscaling services have responsive websites that work perfectly in mobile browsers. You can upload photos directly from your phone's camera roll or photo library, process them on the server, and download the enhanced high-resolution versions back to your phone. This is particularly useful for improving the quality of photos before sharing them on social media or sending them to friends and family.

Does AI upscaling work well on scanned documents?

AI upscaling works on scanned documents but with some important caveats. Text-heavy documents benefit significantly from upscalers that offer a dedicated document or text processing mode, as these modes preserve character edge sharpness much better than general-purpose modes. Photographic images contained within scanned documents, such as old photo albums, upscale well with standard AI models. For pure text documents without images, consider using OCR to extract the text and reformat it rather than upscaling the scanned image, as this produces searchable, editable text.

How long does the AI upscaling process typically take?

Processing time varies based on the image size, upscaling factor, and current server load. A 2x upscale of a standard 1-2 MB image typically completes in 5 to 15 seconds. A 4x upscale of a larger 5-10 MB image may take 30 to 60 seconds or more. If you are processing during peak usage hours, expect longer wait times. Most tools display a progress indicator so you know the processing is actively running.

Can I upscale multiple images at once in batch mode?

Some online AI upscalers support batch processing, which allows you to upload multiple images simultaneously and apply the same upscaling settings to all of them in one operation. This is extremely useful for upscaling an entire photo album from a vacation, a full set of product images for an e-commerce store, or a collection of old scanned family photos. Check the tool's documentation for batch processing limits, as free tiers often restrict the number of images you can process in a single batch.

What image file formats are supported for AI upscaling?

Most tools accept the common web formats including JPEG, PNG, WebP, and BMP. Many also support TIFF for high-quality images and some even accept camera RAW formats from major manufacturers. If your image is in an unusual or proprietary format, convert it to a standard format like PNG or JPEG first for the widest compatibility before uploading to the upscaler.

Key Takeaway

AI image upscaling has democratized the ability to create high-resolution images from low-resolution sources, making it accessible to everyone regardless of their budget or technical skill. The key to consistently great results is simple: start with the absolute best quality source image you can obtain, select the appropriate upscaling factor based on whether the output will be viewed on screen or in print, choose the AI model that matches your specific image type, and always take the time to carefully compare the upscaled result with the original at full magnification before downloading. With these principles in mind, a single pass through a quality AI upscaler will produce results that rival native high-resolution captures, saving you the significant time and expense of reshooting, rescanning, or recreating your images from scratch.

Final Thoughts on AI Image Upscaling

The first time I used a modern AI upscaler I spent a full ten minutes zooming in and out of the result at various magnifications, methodically searching for the artifacts and flaws I fully expected to find based on my experience with traditional enlargement methods. I was genuinely surprised when I could not find any. The image simply looked like it had always been high resolution. That moment changed my entire perspective on what is possible with image processing technology. We have reached a point where we no longer need to reshoot, rescan, or recreate images just because their resolution is too low for modern use.

I encourage you to try an AI image upscaler with one of your own images that you have previously written off as too small or too low quality to use. Find that photo you love but could never print because it was not sharp enough, or that logo you needed but could not enlarge without it looking pixelated. You might be genuinely surprised at what the AI can recover from a file you thought was beyond saving.

AI Image Upscaler: How to Enhance Image Resolution Online Free