BMISP: Bidirectional mapping of image signal processing pipeline

After being processed by the image signal processing (ISP) pipeline in digital cameras, the sRGB im-ages are nonlinear, and thus are not suitable for the computer vision tasks which work best in a linear color space. Therefore, mapping nonlinear sRGB images back to a linear color space is a highly valuable task. To achieve an accurate mapping, this paper proposes a framework based on convolutional neural networks, which models the ISP pipeline in both reverse and forward directions. In particular, for the reverse mapping, a U-net structure is applied to extract features from a given sRGB image, and the ex-tracted features are utilized to estimate the linear and nonlinear transformations in the ISP pipeline. For the forward mapping, the original sRGB image is used as a guidance to embed the camera-style informa-tion. To incorporate the encoded prior knowledge, affine transformations are employed to modulate the features. Experiments demonstrate that the proposed framework is able to achieve the state-of-the-art performance.& COPY; 2023 Elsevier B.V. All rights reserved.