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CRColor: Cycle reference learning for exemplar-based image colorization
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AI
DOI：10.1016/j.neucom.2026.132932.png)
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Abstract
En 中文
Exemplar-based image colorization colorizes a grayscale image based on a color reference image. Although recent advances have significantly improved color matching and generation techniques, there is a paucity of research that addresses the issue of color fidelity, i.e., whether the colored grayscale image accurately preserves the guidance information from the reference image. The absence of a ground truth colored target image for each reference-target image pair renders the color fidelity difficult to quantify or learned by the models. Motivated by this, this paper introduces cyclic strategy into exemplar-based colorization task. Firstly, we propose the concept of cycle reference peak signal-to-noise ratio (CRPSNR). By careful design, the CRPSNR uses the colorization output as the guidance to recolor the reference image. Using the original color reference image as the ground truth, CRPSNR enables the quantification of color fidelity. Furthermore, the cycle reference learning for exemplar-based image colorization (CRColor) is proposed. The CRColor uses a main branch to colorize the target image and a training-only cycle branch to draw the result closer to the guidance, which enables model learning of color fidelity. Experiments demonstrate that our method remains comparable image quality to recent state-of-the-art methods while outperforming the methods in color fidelity to the reference image, both quantitatively and qualitatively. Our code will be published for academic research.
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