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arXiv · 2207.03081

DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning

Ukcheol Shin·Kyunghyun Lee·In So Kweon
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Abstract

In this paper, we propose a multi-objective camera ISP framework that utilizes Deep Reinforcement Learning (DRL) and camera ISP toolbox that consist of network-based and conventional ISP tools. The proposed DRL-based camera ISP framework iteratively selects a proper tool from the toolbox and applies it to the image to maximize a given vision task-specific reward function. For this purpose, we implement total 51 ISP tools that include exposure correction, color-and-tone correction, white balance, sharpening, denoising, and the others. We also propose an efficient DRL network architecture that can extract the various aspects of an image and make a rigid mapping relationship between images and a large number of actions. Our proposed DRL-based ISP framework effectively improves the image quality according to each vision task such as RAW-to-RGB image restoration, 2D object detection, and monocular depth estimation.

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BibTeXRIS

Ukcheol Shin, Kyunghyun Lee, In So Kweon. 2022-07-07. DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning. https://arxiv.org/abs/2207.03081

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