Abstract
This paper presents a novel and efficient image enhancement method based on pigment representation. Unlike conventional methods where the color transformation is restricted to pre-defined color spaces like RGB, our method dynamically adapts to input content by transforming RGB colors into a high-dimensional feature space referred to as \textit{pigments}. The proposed pigment representation offers adaptability and expressiveness, achieving superior image enhancement performance. The proposed method involves transforming input RGB colors into high-dimensional pigments, which are then reprojected individually and blended to refine and aggregate the information of the colors in pigment spaces. Those pigments are then transformed back into RGB colors to generate an enhanced output image. The transformation and reprojection parameters are derived from the visual encoder which adaptively estimates such parameters based on the content in the input image. Extensive experimental results demonstrate the superior performance of the proposed method over state-of-the-art methods in image enhancement tasks, including image retouching and tone mapping, while maintaining relatively low computational complexity and small model size.
Abstract (translated)
本文提出了一种基于色素表示的新型高效的图像增强方法。与传统的方法不同，传统的色彩变换受限于预定义的颜色空间（如RGB），我们的方法通过将RGB颜色动态转换为称为\textit{色素} 的高维特征空间来适应输入内容。提出的色素表示法提供了可调适性和表达性，在图像增强性能方面表现出色。 该方法首先将输入的RGB颜色转换成高维色素，然后单独重新投影并混合这些色素以优化和汇总色素空间中的色彩信息。随后，将这些色素再转换回RGB颜色，生成经过增强的输出图像。变换与重投影参数由视觉编码器自适应地根据输入图像的内容估计得出。 大量的实验结果表明，在包括图像修饰和色调映射在内的图像增强任务中，本文提出的方法在性能上优于现有的方法，并且保持了相对较低的计算复杂度和较小的模型大小。
URL
https://arxiv.org/abs/2510.02713
