# GamutMLP: A Lightweight MLP for Color Loss Recovery

Source: [GamutMLP: A Lightweight MLP for Color Loss Recovery](<https://www.sota2.com/research/paper/68fa6f2b1e209131cea443a8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:28:39.628Z

Authors: Hoang M. Le, Brian Price, Scott Cohen, Michael S. Brown

Published: 2023-04-23T20:26:11.000Z

## Abstract

Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the lost color information is challenging due to the clipping procedure. Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values. GamutMLP takes approximately 2 seconds to optimize and requires only 23 KB of storage. The small memory footprint allows our GamutMLP model to be saved as metadata in the sRGB image -- the model can be extracted when needed to restore wide-gamut color values. We demonstrate the effectiveness of our approach for color recovery and compare it with alternative strategies, including pre-trained DNN-based gamut expansion networks and other implicit neural representation methods. As part of this effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut images for training and testing. Our code and dataset can be found on the project website: https://gamut-mlp.github.io.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Wide-gamut color recovery on ProPhoto 1.0 (test)](<https://www.sota2.com/research/sota/wide-gamut-color-recovery-on-prophoto-1-0-test.md>): Wide-gamut color recovery; ProPhoto 1.0 (test); RMSE: 0.0021.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2304.11743>)

[PDF](<https://arxiv.org/pdf/2304.11743.pdf>)

[Implementation](<https://gamut-mlp.github.io/>)