Enlarge images up to 4x while preserving quality. Uses advanced interpolation algorithms (Bilinear, Bicubic, Lanczos) with optional sharpening.
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Resize and scale images with smart aspect ratio control. Supports custom dimensions, percentage scaling, social media presets, and batch resizing while maintaining image quality.
Convert images to Base64 encoded strings for embedding in CSS, HTML, or JavaScript. Multiple output formats available.
Compare two images with a perceptual engine (pixelmatch) that ignores anti-aliasing, or an exact byte engine. Groups changes into regions with bounding boxes, reports SSIM, and handles size mismatches with an explicit policy.
A 400 px legacy product image must fill an 800 px slot without looking blurry.
Input
product.jpg 400×400 → 2× upscale
Output
product.jpg 800×800 with reconstructed edges, sharper than bilinear
An upscaling model infers plausible detail along edges rather than just averaging neighbours, so text and contours stay crisper than a browser stretch. It runs in the browser, keeping the asset private.
Enlarge images while keeping them sharp. Traditional upscaling produces blurry results because it has to invent pixels that were not in the original. This tool uses enhanced interpolation techniques to produce cleaner, sharper enlargements, especially useful for small images, thumbnails, or low-resolution photos you want to use at larger sizes.
Upscale low-resolution photos from older cameras or phones to usable sizes.
Enlarge small thumbnail images for use in presentations or documents.
Scale up images to meet minimum resolution requirements for printing.
Upscale small pixel art or game sprites while preserving hard edges.
The upscaler offers three classical resampling algorithms and runs whichever you pick directly on canvas pixel data in your browser. Bilinear interpolation is the fastest, blending the four nearest source pixels - fine for photos viewed at modest sizes. Bicubic (the default) weighs a 4x4 neighbourhood with a cubic kernel, producing smoother gradients and fewer stair-step artifacts. Lanczos uses a windowed-sinc filter over a wider 6x6 neighbourhood, which preserves edges best and is the strongest choice for text, line art, and pixel graphics, at the cost of noticeably longer processing - a progress bar tracks the per-pixel loop, since a 4x upscale multiplies the pixel count by 16.
After interpolation you can apply an optional sharpening pass with a 0-100 slider, implemented as a 3x3 unsharp-mask convolution whose strength scales with the slider. It counteracts the slight softness any interpolation introduces; moderate values usually recover perceived crispness without adding halos around high-contrast edges. The result is always encoded as PNG regardless of the input format, and the download is named after the original file with the factor appended, for example photo_2x.png - lossless output means no new compression artifacts are stacked on top of whatever the source already had.
A built-in comparison mode overlays the original and the upscaled result with a draggable split handle, so you can sweep across the image and judge edge quality at the boundary instead of flipping between two views from memory. The 2x, 3x, and 4x factors apply exactly (a 400x400 source becomes 800x800, 1200x1200, or 1600x1600), and because everything runs through the HTML Canvas API on your machine, the image is never uploaded.
Regular resizing uses basic interpolation that blurs edges. The upscaler uses smarter algorithms that preserve edges and add detail more intelligently.
2x is safest for most images. Going higher works better on images with simple content or hard edges (like pixel art). Photographic detail degrades more at higher factors.
No algorithm can create information from nothing. The upscaler makes educated guesses about what higher-resolution detail might look like, producing sharper results than simple interpolation.
Images are decoded, edited, and exported entirely inside this browser tab. No originals, exports, or metadata are uploaded.