论文标题
DRL-ISP：具有深度加固学习的多目标相机ISP
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning
论文作者
论文摘要
在本文中，我们提出了一个多目标相机ISP框架，该框架利用深入增强学习（DRL）和ISP工具箱，该工具箱由基于网络和常规的ISP工具组成。提出的基于DRL的相机ISP框架迭代从工具箱中选择了一个合适的工具，并将其应用于图像以最大化给定的特定于任务特定的奖励功能。为此，我们总共实施了51个ISP工具，包括曝光校正，颜色和色调校正，白平衡，锐化，脱氧和其他工具。我们还提出了一个有效的DRL网络体系结构，该体系结构可以提取图像的各个方面，并在图像和大量操作之间建立刚性映射的关系。我们提出的基于DRL的ISP框架可有效地根据每个视觉任务（例如原始视觉到RGB图像恢复，2D对象检测和单眼深度估计）提高图像质量。
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.
