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. The neural network was trained using a large image database of raw
Bayer images. 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.

使用全卷积神经网络对摄像机拍摄的原始 Bayer 图像进行缺陷像素纠正、去噪、白平衡、曝光校正、解模糊、颜色转换和伽马编码的端到端处理，此神经网络是第一个进行完整图像信号处理流程处理的。通过实验，结果表明该系统的性能优于一般传统的处理流程。