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Traditional image signal processing (ISP) pipeline consists of a set ofindividual image processing components onboard a camera to reconstruct ahigh-quality sRGB image from the sensor raw data. Due to the hand-craftednature of the ISP components, traditional ISP pipeline has limitedreconstruction quality under challenging scenes. Recently, the convolutionalneural networks (CNNs) have demonstrated their competitiveness in solving manyindividual image processing problems, such as image denoising, demosaicking,white balance and contrast enhancement. However, it remains a question whethera CNN model can address the multiple tasks inside an ISP pipelinesimultaneously. We make a good attempt along this line and propose a novelframework, which we call CameraNet, for effective and general ISP pipelinelearning. The CameraNet is composed of two CNN modules to account for two setsof relatively uncorrelated subtasks in an ISP pipeline: restoration andenhancement. To train the two-stage CameraNet model, we specify twogroundtruths that can be easily created in the common workflow of photography.CameraNet is trained to progressively address the restoration and theenhancement subtasks with its two modules. Experiments show that the proposedCameraNet achieves consistently compelling reconstruction quality on threebenchmark datasets and outperforms traditional ISP pipelines.

CameraNet: A Two-Stage Framework for Effective Camera ISP Learning