Color Matching Using Hypernetwork-Based Kolmogorov-Arnold Networks

Abstract

We present cmKAN, a versatile framework for color matching. Given an input image with colors from a source color distribution, our method effectively and accurately maps these colors to match a target color distribution in both supervised and unsupervised settings. Our framework leverages the spline capabilities of Kolmogorov-Arnold Networks (KANs) to model the color matching between source and target distributions. Specifically, we developed a hypernetwork that generates spatially varying weight maps to control the nonlinear splines of a KAN, enabling accurate color matching. As part of this work, we introduce a large-scale dataset of paired images captured by two distinct cameras to evaluate our method’s efficacy in matching colors produced by different cameras. We evaluated our approach across various color-matching tasks, including: (1) raw-to-raw mapping, where the source color distribution is in one camera’s raw color space and the target in another camera’s raw space; (2) raw-to-sRGB mapping, where the source color distribution is in a camera’s raw space and the target is in the display sRGB space, emulating the color rendering of a camera ISP; and (3) sRGB-to-sRGB mapping, where the goal is to transfer colors from a source sRGB space (e.g., produced by a source camera ISP) to a target sRGB space (e.g., from a different camera ISP). The results demonstrate that our method achieves state-of-the-art performance across these tasks while remaining lightweight compared to other color matching and transfer methods.

Dataset

How To Use

Our cmKAN provides a command-line interface (CLI) to interact with the following tools:

python main.py -h 

Usage: cmKAN CLI [-h] {data-create,test,train,predict,unit-test} ...

Options:
  -h, --help            Show this help message and exit

Tools:
  {data-create,test,train,predict,unit-test}
    
    data-create         Create dataset
    train               Train model
    test                Test model
    predict             Run model inference
    unit-test           Run unit tests

For all the tools, you can use the -h flag to get help on how to use them (e.g. python main.py train -h). Here are some examples on how to use the tools:

Train

python main.py train -c configs/config.yaml

Test

python main.py test -c configs/config.yaml -w checkpoint.ckpt

Predict

python main.py predict -c configs/config.yaml -i path/to/input/folder -o path/to/output/folder