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

Matheus Henrique Marques da Silva, Jhessica Victoria Santos da Silva, Rodrigo Reis Arrais, Wladimir Barroso Guedes de Araújo Neto, Leonardo Tadeu Lopes +7 more
5/19/2023
cs.LGeess.IV

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Abstract

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.

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Cite this paper

@article{silva2023meets,
  title  = {ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing},
  author = {Matheus Henrique Marques da Silva and Jhessica Victoria Santos da Silva and Rodrigo Reis Arrais and Wladimir Barroso Guedes de Araújo Neto and Leonardo Tadeu Lopes and Guilherme Augusto Bileki and Iago Oliveira Lima and Lucas Borges Rondon and Bruno Melo de Souza and Mayara Costa Regazio and Rodolfo Coelho Dalapicola and Claudio Filipi Gonçalves dos Santos},
  year   = {2023},
  eprint = {2305.11994},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.11994v2}
}

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