A conventional camera performs various signal processing steps sequentially to reconstruct an image from a raw Bayer image. When performing these processing in multiple stages the residual error from each stage accumulates in the image and degrades the quality of the final reconstructed image. In this paper, we present a fully convolutional neural network (CNN) to perform defect pixel correction, denoising, white balancing, exposure correction, demosaicing, color transform, and gamma encoding. To our knowledge, this is the first CNN trained end-to-end to perform the entire image signal processing pipeline in a camera. Through extensive experiments, we show that the proposed CNN based image signal processing system performs better than the conventional signal processing pipelines that perform the processing sequentially.
Ratnasingam REF have combined defect pixel interpolation, demosaicing, denoising, white balancing, exposure correction, and contrast enhancement using a single CNN.
Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Computer Science Engineering