WBFlow: Few-shot White Balance for sRGB Images via Reversible Neural Flows

Abstract

The sRGB white balance methods aim to correct the nonlinear color cast of sRGB images without accessing raw values. Although existing methods have achieved increasingly better results, their generalization to sRGB images from multiple cameras is still under explored. In this paper, we propose the network named WBFlow that not only performs superior white balance for sRGB images but also generalizes well to multiple cameras. Specifically, we take advantage of neural flow to ensure the reversibility of WBFlow, which enables lossless rendering of color cast sRGB images back to pseudo raw features for linear white balancing and thus achieves superior performance. Furthermore, inspired by camera transformation approaches, we have designed a camera transformation (CT) in pseudo raw feature space to generalize WBFlow for different cameras via few shot learning. By utilizing a few sRGB images from an untrained camera, our WBFlow can perform well on this camera by learning the camera specific parameters of CT. Extensive experiments show that WBFlow achieves superior camera generalization and accuracy on three public datasets as well as our rendered multiple camera sRGB dataset. Our code is available at https://github.com/ChunxiaoLe/WBFlow.