Two-stage deep learning framework for sRGB image white balance

Abstract

This work aims to correct white-balance errors in sRGB images. These white-balance errors are hard to fix due to the nonlinear color-processing procedures applied by camera image signal processors (ISP) to produce the final sRGB colors. Camera ISPs apply these nonlinear procedures after the essential white-balance step to render sensor raw images to the sRGB space through a camera-specific set of tone curves and look-up tables. To correct improperly white-balanced images, projecting non-linear sRGB colors back to their original raw space is required. Recent work formulates the problem as an image translation problem, where input sRGB colors are mapped using nonlinear polynomial correction functions to fix such white-balance errors. In this work, we show that correcting white-balance errors in sRGB images through a global color mapping followed by spatially local adjustments, learned in an end-to-end training, introduces perceptual improvements in the final results. Qualitative and quantitative comparisons with recently published methods for camera-rendered image white balancing validate our method’s efficacy and show that our method achieves competitive results with state-of-the-art methods.