Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain
Xuanhua He Tao Hu Guoli Wang Zejin Wang Run Wang Qian Zhang Keyu Yan Ziyi Chen Rui Li Chenjun Xie Jie Zhang Man Zhou
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}.