PNG Noise Adder

Uploads a PNG, decodes it onto an off-screen canvas, and perturbs every pixel's red, green, and blue values with reproducible pseudo-random noise at a chosen intensity, leaving the alpha channel untouched, then lets you download the result. A free online tool from Staaarter, right in your browser.

Runs locallyUpdated 2026-07-26

Overview

Introduction

This tool adds random grain to a PNG entirely in your browser, using a seeded pseudo-random number generator so the effect is reproducible rather than different every time.

It's a quick way to give a flat, computer-generated image a more organic or photographic texture.

What Is PNG Noise Adder?

A client-side noise generator that reads an uploaded PNG's pixel data and nudges each pixel's red, green, and blue values by a small random amount, controlled by an intensity slider and a numeric seed.

The alpha channel is left completely unchanged, so transparency in the source image is preserved exactly.

How PNG Noise Adder Works

The tool seeds a small, fast pseudo-random number generator (mulberry32) with your chosen seed, then for every pixel draws a random delta in the range plus-or-minus (intensity percent of 255) for each of the red, green, and blue channels and adds it to the original value, clamping the result back into the valid byte range.

Because the generator is deterministic, the same seed and intensity always produce byte-for-byte identical noise.

When To Use PNG Noise Adder

Use it to add film-grain texture to a clean illustration, dither a smooth gradient to reduce visible color banding, or generate quick synthetic test images with controlled randomness.

It's also handy for stress-testing other image tools with non-uniform pixel data.

Often used alongside PNG Opacity Changer and PNG Quality Changer.

Features

Advantages

  • Runs entirely client-side; the uploaded image is never sent to a server.
  • Deterministic and reproducible via the seed value, unlike most noise filters.
  • Alpha channel is always preserved, so transparent PNGs stay transparent.

Limitations

  • The noise is uncorrelated per-pixel (not clustered like real film grain), so it may look more like static than authentic photographic grain at high intensity.
  • At very high intensity, noise can wash out fine image detail since the random deltas can span most of the 0-255 range.

Examples

Add subtle grain to a flat illustration

Input

A smooth 400x400 PNG gradient, intensity 15, seed 1

Output

The same gradient with a light, reproducible speckle texture across all color channels

A low intensity keeps the deltas small, adding texture without destroying the underlying gradient.

Regenerate the same noisy result later

Input

Any PNG, intensity 40, seed 7 (run twice)

Output

Byte-for-byte identical noisy output both times

Because the random generator is seeded, the same seed and intensity always reproduce the same noise pattern.

Best Practices & Notes

Best Practices

  • Start with a low intensity (10-20) and increase gradually; noise effects compound quickly.
  • Note down the seed value if you want to reproduce or fine-tune the exact same noise pattern later.

Developer Notes

The noise math lives in a pure lib function using a seeded mulberry32 PRNG (no Math.random, so results are reproducible in tests and across runs) operating directly on the Uint8ClampedArray buffer; only the alpha byte at each pixel is skipped.

PNG Noise Adder Use Cases

  • Adding film-grain texture to a digital illustration
  • Dithering a smooth gradient to reduce color banding
  • Generating reproducible synthetic test images

Common Mistakes

  • Expecting two different seeds to look similar at the same intensity — different seeds produce entirely different noise patterns, not just different strengths.
  • Setting intensity too high and losing the original image's detail under the noise.

Tips

  • Use a low intensity combined with the opacity changer to blend a subtle grain overlay.
  • If you need consistent noise across a batch of images, reuse the exact same seed value for each.

References

Frequently Asked Questions