Low-quality Stamp Fixer

Uploads a stamp PNG, decodes it onto an off-screen canvas, applies an unsharp-mask-style sharpening pass to recover crisper edges, and optionally upscales it with nearest-neighbor resampling, then lets you download the result. This enhances existing edges and enlarges the image; it can't invent detail that was never captured. A free online tool from Staaarter, right in your browser.

Runs locallyUpdated 2026-07-26

Overview

Introduction

This tool improves the visual quality of a blurry or low-resolution stamp scan entirely in your browser, by composing two real pixel operations: sharpening and upscaling.

It's an honest enhancement tool, not an AI upscaler — it can make existing edges crisper and the image physically larger, but it can't add detail that was never captured in the source.

What Is Low-quality Stamp Fixer?

A client-side image-enhancement tool that first applies an unsharp-mask-style 3x3 convolution to boost local edge contrast, then optionally resizes the result larger using nearest-neighbor resampling.

Both steps reuse this category's existing sharpen and upscale lib functions rather than introducing new pixel math.

How Low-quality Stamp Fixer Works

The sharpen pass boosts each pixel relative to its four orthogonal neighbors using a 'plus' convolution kernel, amplifying contrast at edges so faint or slightly blurred stamp lines read more clearly.

If an upscale percentage above 100 is requested, the already-sharpened image is then resized larger with nearest-neighbor sampling, which enlarges without introducing new blur.

When To Use Low-quality Stamp Fixer

Use it on a stamp photo or scan that came out slightly soft or was captured at low resolution, before using it in a document or design.

It's also useful as a final cleanup pass after other stamp edits, if resizing or recoloring left edges looking a bit soft.

Features

Advantages

  • Runs entirely client-side; the uploaded stamp is never sent to a server.
  • Composes two already-tested pixel operations instead of introducing new, unverified math.
  • Sharpening happens before upscaling, which avoids amplifying upscale blockiness.

Limitations

  • Cannot recover detail that was never captured in the original photo or scan; it's a real contrast/edge enhancement, not AI super-resolution.
  • Aggressive sharpening on a noisy or JPEG-artifacted source can introduce visible haloing around edges.

Examples

Sharpen a soft phone photo of a stamp

Input

A slightly blurry stamp photo, sharpen 120, upscale 100%

Output

The same-size image with visibly crisper edges

A moderate sharpen amount recovers edge definition without introducing much noise.

Enlarge and clean up a tiny scanned stamp

Input

A 100x100 low-res stamp scan, sharpen 80, upscale 300%

Output

A 300x300 image, sharpened before being enlarged

Sharpening first keeps edges cleaner than sharpening after the upscale would.

Best Practices & Notes

Best Practices

  • Start with a moderate sharpen amount (80-120) and increase gradually while watching for haloing artifacts.
  • Only upscale as much as you actually need; heavier upscaling makes nearest-neighbor blockiness more visible.

Developer Notes

This lib file composes two already-shipped, independently tested pure functions — sharpenPng and upscalePng — rather than writing new convolution or resampling math; sharpening always runs before the optional upscale step so the kernel samples the original pixel density.

Low-quality Stamp Fixer Use Cases

  • Cleaning up a slightly blurry phone photo of a physical stamp
  • Enlarging a small scanned stamp for use in a bigger document layout
  • Sharpening edges that softened after other pixel-editing steps

Common Mistakes

  • Expecting the tool to add detail that simply wasn't captured in the original image.
  • Over-sharpening a noisy scan, which amplifies grain and JPEG artifacts along with real edges.

Tips

  • If the result looks too harsh, lower the sharpen amount rather than the upscale percentage.
  • For a very small source image, consider rescanning at a higher resolution instead of relying solely on upscaling.

References

Frequently Asked Questions