Restore Scanned Photo — Fix Scan Artifacts and Enhance Quality
Restore scanned photos that suffer from scanning artifacts — moire patterns, dust spots, scan lines, color casts, and resolution limits. AI identifies and removes scan-specific issues while enhancing the underlying photo quality.
Free local tracing needs no account. AI tools use credits and upload files for processing.
Explore features and examplesFrom your image to editable paths
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Open your file
PNG, JPG, WebP, AVIF, GIF, or BMP up to 10 MB in the free workspace (the AI converter takes PNG, JPG, WebP, or AVIF). Existing SVGs can be edited in the free workspace.
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Trace and inspect
Preview the shapes and colors. Clear logos and flat artwork usually trace more predictably than photos.
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Make it yours
Edit the SVG, compare optional optimization, and download your result.
Working with your SVG
May reduce visible scratches and stains
May reduce visible scratches and stains
In Your Browser
Upload a PNG, JPEG, or WebP file and download the result when processing finishes. Nothing to install.
Source-dependent color adjustment
Source-dependent color adjustment
No Watermark
Download the result without a watermark, then check it before you use it. Results depend on the source image.
JPG, PNG, and WebP input
JPG, PNG, and WebP input
Multi-Tool Platform
After processing, use our other AI tools — upscaling, restoration, vectorization — all in one platform with shared credits.
AI conversion features
- Process Scanned Photo files directly
- May reduce visible scratches and stains
- Source-dependent color adjustment
- May enhance visible facial detail
- Noise and grain reduction
- JPG, PNG, and WebP input
- No software installation required
- Works in any modern browser
- Commercial use allowed
- Pay-per-use — no subscription
- 1 free credit after you verify your email
Questions about your file
Free local tools. Optional AI credits.
Local tracing and SVG editing need no credits. AI conversion includes one credit after email verification; additional packs start at $9.99.
Scanner-Specific Artifacts and How They Differ from Digital Photo Problems
Scanned photos have a unique set of quality problems that digital photos never encounter. The most distinctive is moire patterns — a wavy, rainbow-like interference pattern that appears when a scanner's sensor grid interacts with the halftone dot pattern used in printed photographs. This is especially severe when scanning photos from newspapers, magazines, and some older photo prints that were made using halftone printing. The moire pattern is not in the original photo; it is created by the scanning process itself.
Physical damage is the other category of scanner-specific problems. Dust particles on the scanner glass or the photo surface appear as white or dark spots. Scratches on the photo show as thin lines. Fingerprints create smudged areas. Creases and fold marks show as sharp lines with color shifts. Yellowing from age adds a warm color cast to the entire image. Water damage can cause spots, staining, or areas of lost emulsion. None of these exist in digital photos — they are physical-world artifacts captured by the scanning process.
Scanner hardware also introduces its own limitations. Flatbed scanners have a fixed optical resolution (typically 300-4800 DPI for consumer models), and scanning above this native resolution just interpolates data rather than capturing real detail. CIS (Contact Image Sensor) scanners are thinner and cheaper but produce less sharp results than CCD scanners. Scanning with the lid open (for thick books or framed photos) can introduce light leaks and uneven illumination. Each of these scanner-specific issues requires different AI processing strategies than digital photo enhancement.
Pro Tips for Better Results
Scan at the highest optical resolution your scanner supports
Before scanning, check your scanner's native optical resolution (not interpolated). Scan at this maximum optical DPI — typically 600 or 1200 DPI for consumer flatbed scanners. Scanning at higher "interpolated" resolutions adds no real detail and just increases file size. Our AI can upscale a clean 600 DPI scan more effectively than it can fix a noisy 2400 DPI interpolated scan.
Clean the scanner glass and the photo before scanning
Use a microfiber cloth on the scanner glass and gently blow compressed air on the photo surface. Every dust particle and fingerprint on either surface becomes a permanent artifact in the scan. Spending 30 seconds cleaning before scanning saves significant restoration effort afterward.
Scan printed photos (newspapers, magazines) at an angle if possible
Moire patterns occur because the scanner's sampling grid aligns with the halftone dot grid of the print. Rotating the photo 15-30 degrees on the scanner bed can break this alignment and dramatically reduce moire. You can rotate the image back to straight in any editor after scanning.
Save scans as TIFF or PNG, not JPEG
Scanned photos already have quality issues — adding JPEG compression on top introduces additional artifacts that make restoration harder. Save your raw scans as TIFF (best) or PNG (good). If you already have JPEG scans, upload them as-is — the AI handles both scan artifacts and JPEG artifacts, but starting with a lossless scan format gives better results.
Why Moire Patterns Appear in Scanned Photos and How AI Removes Them
Moire patterns are an aliasing artifact caused by two overlapping periodic patterns interfering with each other. In scanned photos, the first pattern is the halftone dot screen used to print the original photo (typically 85-150 lines per inch for magazines and newspapers), and the second is the scanner's CCD or CIS sensor array. When the spatial frequencies of these two patterns interact, they create a low-frequency interference pattern visible as colored waves or diamond shapes. Traditional moire removal uses frequency-domain filtering (notch filters in the Fourier transform to suppress the halftone frequency), but this can also remove real image detail at those frequencies. Our AI approach is superior because it learns to distinguish moire patterns from real image content using contextual understanding — it knows that the wavy color bands in a sky region are moire (because skies are smooth) while preserving similarly-frequency detail in textured regions like fabric or foliage.