Raw Image Based Over-Exposure Correction Using Channel-Guidance Strategy

Most existing methods for over-exposure in image correction are developed based on sRGB images, which can result in complex and non-linear degradation due to the image signal processing pipeline. By contrast, data-driven approaches based on RAW image data offer natural advantages for image processing tasks. RAW images, characterized by their near-linear correlation with scene radiance and enriched information content due to higher bit depth, demonstrate superior performance compared to sRGB-based techniques. Further, the spectral sensitivity characteristics intrinsic to digital camera sensors indicate that the blue and red channels in a Bayer pattern RAW image typically encompass more contextual information than the green channels. This property renders them less susceptible to over-exposure, thereby making them more effective for data extraction in high dynamic range scenes. In this paper, we introduce a Channel-Guidance Network (CGNet) that leverages the benefits of RAW images for over-exposure correction. The CGNet estimates the properly-exposed sRGB image directly from the over-exposed RAW image in an end-to-end manner. Specifically, we introduce a RAW-based channel-guidance branch to the U-net-based backbone, which exploits the color channel intensity prior of RAW images to achieve superior over-exposure correction performance. To further facilitate research in over-exposure correction, we present synthetic and real-world over-exposure correction benchmark datasets. These datasets comprise a large set of paired RAW and sRGB images across a variety of scenarios. Experiments on our RAW-sRGB datasets validate the advantages of our RAW-based channel guidance strategy and proposed CGNet over state-of-the-art sRGB-based methods on over-exposure correction. Our code and dataset are publicly available at
https://github.com/whiteknight-WJN/CGNet
.