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- W4313006148 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." @default.
- W4313006148 created "2023-01-05" @default.
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- W4313006148 date "2022-10-23" @default.
- W4313006148 modified "2023-10-16" @default.
- W4313006148 title "DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning" @default.
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- W4313006148 doi "https://doi.org/10.1109/iros47612.2022.9981361" @default.
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