CInvISP: Conditional Invertible Image Signal Processing Pipeline

Standard RGB (sRGB) images processed by the image signal processing (ISP) pipeline of digital cameras have a nonlinear relationship with the scene irradiance. Therefore, the low-level vision tasks which work best in a linear color space are not suitable to be carried out in the sRGB color space. To address this issue, this paper proposes an approach called CInvISP to provide a bidirectional mapping between the nonlinear sRGB and linear CIE XYZ color spaces. To ensure a fully invertible ISP, the basic building blocks in our framework adopt the structure of invertible neural network. As camera-style information is embedded in sRGB images, it is necessary to completely remove it during backward mapping, and properly incorporate it during forward mapping. To this end, a conditional vector is extracted from the sRGB input and inserted into each invertible building block. Experiments show that compared to other mapping approaches, CInvISP achieves a more accurate bidirectional mapping between the two color spaces. Moreover, it is also verified that such a precise bidirectional mapping facilitates low-level vision tasks including image denoising and retouching well.