  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.
