How to Enhance Old, Blurry, or Low-Resolution Photos

Old family photographs, scanned prints, blurry phone shots, and low-resolution images saved years ago are all candidates for AI enhancement. The technology has improved dramatically — results that weren't possible two years ago are now achievable in seconds with free tools.

Here's what you can realistically expect from image enhancement, and how to get the best results.

What AI image enhancement actually does

AI image enhancement tools use machine learning models trained on millions of image pairs — low-resolution originals alongside their high-resolution versions. The model learns the patterns that distinguish sharp, detailed images from blurry or low-resolution ones, and applies those patterns to enhance new images it hasn't seen before.

The key distinction: AI enhancement doesn't recover information that was never captured. If a photo was out of focus, the detail that would have been in focus simply doesn't exist in the image data. What AI does is make intelligent guesses about what that detail probably looked like, based on patterns in similar images it was trained on.

For faces, the results are often remarkable — the model has seen so many faces that it can plausibly reconstruct facial detail from blur. For unusual subjects, unique textures, or fine architectural detail, results vary more.

What works well

Old scanned photos: Scanned prints from the 1970s–90s are often low resolution by modern standards but are actually sharp at their original print size. AI enhancement can increase the digital resolution significantly, making them suitable for reprinting at larger sizes than the original scan would allow.

Slightly blurry portraits: Motion blur or slight focus errors in portraits can often be partially corrected — particularly for faces, where the AI's training data is richest.

Low-resolution photos saved from old phones or early digital cameras: Photos from 2–3 megapixel cameras from the early 2000s can be upscaled to modern resolution standards with good results.

Screenshots and images from the web: Low-resolution images found online — product photos, article images, reference images — often improve significantly with enhancement.

What works less well

Severely out-of-focus images: Heavy blur obscures too much information for any tool to reconstruct convincingly. Enhancement will reduce the blur somewhat but won't produce a sharp image.

Very dark or heavily underexposed photos: Noise and lack of detail in dark areas is difficult for AI to reconstruct. Exposure correction before enhancement helps, but severely underexposed images have fundamental data limitations.

Heavy motion blur: Motion blur in a specific direction is different from focus blur and is harder for AI to reverse. Slight motion blur improves; heavy trails don't.

Images with heavy compression artifacts: JPEG images saved at very low quality settings have blocky artifacts baked in. Enhancement can reduce these artifacts somewhat but can't fully restore the original smoothness.

How to get the best results from BG Bueno's Image Enhancer

1. 1Start with the best version of the image you have. If you have an original scan, use that rather than a photograph of the scan.
2. 2Crop tightly to the subject before enhancing if you're interested in a specific area. Enhancement quality is often better when the subject fills more of the frame.
3. 3Don't over-enhance. Running an image through enhancement multiple times can introduce artifacts. One pass is usually the right call.
4. 4Use the comparison view to evaluate whether the enhancement actually improved things before downloading.

Scanning old prints for best enhancement results

If you're starting with physical photographs, the quality of your scan directly determines how well enhancement will work:

• •Scan at 600 DPI minimum. 1200 DPI for small prints (wallet size, passport photos from the past).
• •Use a flatbed scanner if possible — phone camera scans introduce additional blur and perspective distortion.
• •Clean the scanner glass and the print surface before scanning. Dust appears as specks in the image.
• •Scan as PNG or TIFF, not JPG — avoid introducing compression artifacts before enhancement.

Realistic expectations

AI image enhancement is genuinely useful and produces results that would have required professional retouching software and significant skill just a few years ago. But it's still based on plausible reconstruction, not actual data recovery.

The best results come from images that are almost good — slightly soft, slightly low-resolution, slightly underexposed — where a small improvement makes a big difference. For severely degraded images, enhancement is still worth trying, but the results will be more variable.