Abstract
Full DNN-based image signal processors (ISPs) have been actively studied and have achieved superior image quality compared to conventional ISPs. In contrast to this trend, we propose a lightweight ISP that consists of simple conventional ISP functions but achieves high image quality by increasing expressiveness. Specifically, instead of tuning the parameters of the ISP, we propose to control them dynamically for each environment and even locally. As a result, state-of-the-art accuracy is achieved on various datasets, including other tasks like tone mapping and image enhancement, even though ours is lighter than DNN-based ISPs. Additionally, our method can process different image sensors with a single ISP through dynamic control, whereas conventional methods require training for each sensor.
Abstract (translated)
完整的基于深度神经网络（ISPs）图像信号处理器（ISPs）已积极研究，并比传统ISPs获得更好的图像质量。相比之下，我们提出了一个轻量级的ISP，它由简单的传统ISP功能组成，通过增加表现力来实现高图像质量。具体来说，我们动态地控制ISP的参数，甚至局部控制。因此，在包括其他任务（如色调映射和图像增强）的各种数据集上，尽管我们的ISP比基于深度神经网络的ISP更轻，但最高精度仍得以实现。此外，通过动态控制，我们的方法可以处理不同图像传感器，而传统方法需要对每个传感器进行训练。
URL
https://arxiv.org/abs/2403.10091
