Color Matching Using Hypernetwork-Based Kolmogorov-Arnold Networks

• URL: http://arxiv.org/abs/2503.11781v1
• Date: Fri, 14 Mar 2025 18:17:19 GMT
• Title: Color Matching Using Hypernetwork-Based Kolmogorov-Arnold Networks
• Authors: Artem Nikonorov, Georgy Perevozchikov, Andrei Korepanov, Nancy Mehta, Mahmoud Afifi, Egor Ershov, Radu Timofte,
• Abstract summary: cmKAN is a versatile framework for color matching.<n>We use Kolmogorov-Arnold Networks (KANs) to model the color matching between source and target distributions.<n>We introduce a first large-scale dataset of paired images captured by two distinct cameras.
• Score: 44.97307414849601
• License: http://creativecommons.org/licenses/by/4.0/
• 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 first large-scale dataset of paired images captured by two distinct cameras and evaluate the efficacy of our and existing methods in matching colors. 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 show that our method outperforms existing approaches by 37.3% on average for supervised and unsupervised cases while remaining lightweight compared to other methods. The codes, dataset, and pre-trained models are available at: https://github.com/gosha20777/cmKAN

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