We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable neural color operators. The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar. To reflect the homomorphism property of color operators, we employ equivariant mapping and adopt an encoder-decoder structure which maps the non-linear color transformation to a much simpler transformation (i.e., translation) in a high dimensional space. The scalar strength of each neural color operator is predicted using CNN based strength predictors by analyzing global image statistics. Overall, our method is rather lightweight and offers flexible controls. Experiments and user studies on public datasets show that our method consistently achieves the best results compared with SOTA methods in both quantitative measures and visual qualities. The code and data will be made publicly available.

本研究提出了一种使用可训练的神经彩色算子来模拟传统彩色算子的影像修复方法，使用编码器 - 解码器结构来将非线性的颜色变换映射到高维空间的简单变换，通过全局图像统计分析来使用CNN强度预测器预测每个神经彩色算子的标量强度，经实验和用户研究证明，该方法在定量测量和可视化质量方面都能显著提高修复效果。

序列图像修饰的神经色彩算子