ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing

The Image Signal Processor (ISP) is a component of digital cameras capable of performing various tasks to improve image quality, as demosaicing, denoising, and white balance. The set of tasks performed by the ISP is called ISP pipeline, divided in preproccessing and postprocessing steps, and may differ from manufacturer to manufacturer [1]. Nowadays, Machine Learning is used to replace partially or the entire ISP pipeline. Particulary, Deep Learning is employed to replace ISP tasks, working on noise removal or some image feaure that hinders processing over the network. Deep Learning network provides an improvement in relation to computational efficiency and processing time. This survey paper aims to analyze recent studies, 27 research papers, that implemented Deep Learning based ISP pipeline.