# GamutMLP: A Lightweight MLP for Color Loss Recovery

arXiv: https://arxiv.org/abs/2304.11743 · HTML: https://mlsanity.com/paper/2304.11743/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline](https://mlsanity.com/paper/2004.01179/) (2020)
- [Real-time Neural Radiance Caching for Path Tracing](https://mlsanity.com/paper/2106.12372/) (2021)
- [Spatially-Adaptive Pixelwise Networks for Fast Image Translation](https://mlsanity.com/paper/2012.02992/) (2020)
- [Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata](https://mlsanity.com/paper/2206.01813/) (2022)
- [Unsupervised Learning for Color Constancy](https://mlsanity.com/paper/1712.00436/) (2017)
- [Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images](https://mlsanity.com/paper/1311.6887/) (2013)
- [Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network](https://mlsanity.com/paper/1707.08350/) (2017)

## Built on by (most cited first, 5 of 5)

- [Color Matching Using Hypernetwork-Based Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2503.11781/) (2025)
- [INRetouch: Context Aware Implicit Neural Representation for Photography Retouching](https://mlsanity.com/paper/2412.03848/) (2024)
- [RAWild: Sensor-Agnostic RAW Object Detection via Physics-Guided Curve and Grid Modeling](https://mlsanity.com/paper/2605.05941/) (2026)
- [Addressing Image Authenticity When Cameras Use Generative AI](https://mlsanity.com/paper/2604.21879/) (2026)
- [Color Pass-Through via Camera-Display Coupling](https://mlsanity.com/paper/2607.12746/) (2026)

## Abstract (arXiv)

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.