ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing
It addresses the problem of improving camera image processing for researchers and practitioners, but it is incremental as it reviews existing methods without introducing new techniques.
This survey investigates deep learning methods for Image Signal Processing (ISP), analyzing recent research that tackles tasks like demosaicing, denoising, and enhancement, and provides comparisons and improvement points for future work.
The entire Image Signal Processor (ISP) of a camera relies on several processes to transform the data from the Color Filter Array (CFA) sensor, such as demosaicing, denoising, and enhancement. These processes can be executed either by some hardware or via software. In recent years, Deep Learning has emerged as one solution for some of them or even to replace the entire ISP using a single neural network for the task. In this work, we investigated several recent pieces of research in this area and provide deeper analysis and comparison among them, including results and possible points of improvement for future researchers.