  Traditional image signal processing (ISP) pipeline consists of a set of
individual image processing components onboard a camera to reconstruct a
high-quality sRGB image from the sensor raw data. Due to the hand-crafted
nature of the ISP components, traditional ISP pipeline has limited
reconstruction quality under challenging scenes. Recently, the convolutional
neural networks (CNNs) have demonstrated their competitiveness in solving many
individual image processing problems, such as image denoising, demosaicking,
white balance and contrast enhancement. However, it remains a question whether
a CNN model can address the multiple tasks inside an ISP pipeline
simultaneously. We make a good attempt along this line and propose a novel
framework, which we call CameraNet, for effective and general ISP pipeline
learning. The CameraNet is composed of two CNN modules to account for two sets
of relatively uncorrelated subtasks in an ISP pipeline: restoration and
enhancement. To train the two-stage CameraNet model, we specify two
groundtruths that can be easily created in the common workflow of photography.
CameraNet is trained to progressively address the restoration and the
enhancement subtasks with its two modules. Experiments show that the proposed
CameraNet achieves consistently compelling reconstruction quality on three
benchmark datasets and outperforms traditional ISP pipelines.
