RAW to sRGB mapping, which aims to convert RAW images from smartphones into RGB form equivalent to that of Digital Single-Lens Reflex (DSLR) cameras, has become an important area of research. However, current methods often ignore the difference between cell phone RAW images and DSLR camera RGB images, a difference that goes beyond the color matrix and extends to spatial structure due to resolution variations. Recent methods directly rebuild color mapping and spatial structure via shared deep representation, limiting optimal performance. Inspired by Image Signal Processing (ISP) pipeline, which distinguishes image restoration and enhancement, we present a novel Neural ISP framework, named FourierISP. This approach breaks the image down into style and structure within the frequency domain, allowing for independent optimization. FourierISP is comprised of three subnetworks: Phase Enhance Subnet for structural refinement, Amplitude Refine Subnet for color learning, and Color Adaptation Subnet for blending them in a smooth manner. This approach sharpens both color and structure, and extensive evaluations across varied datasets confirm that our approach realizes state-of-the-art results. Code will be available at ~\url{https://github.com/alexhe101/FourierISP}.

手机相机图像处理中RAW转sRGB映射是一项重要的研究领域，现有方法往往忽视了手机RAW图像和单反相机RGB图像之间的差异，我们提出了一种名为FourierISP的新型神经ISP框架，该方法通过在频域内独立优化图像的风格和结构，使得色彩和结构同时得到增强，广泛的数据集评估表明我们的方法达到了最先进的效果。

傅里叶域中的解耦样式结构增强RAW至sRGB