Image Signal Processors (ISPs) play important roles in image recognition tasks as well as in the perceptual quality of captured images. In most cases, experts make a lot of effort to manually tune many parameters of ISPs, but the parameters are sub-optimal. In the literature, two types of techniques have been actively studied: a machine learning-based parameter tuning technique and a DNN-based ISP technique. The former is lightweight but lacks expressive power. The latter has expressive power, but the computational cost is too heavy on edge devices. To solve these problems, we propose "DynamicISP," which consists of multiple classical ISP functions and dynamically controls the parameters of each frame according to the recognition result of the previous frame. We show our method successfully controls the parameters of multiple ISP functions and achieves state-of-the-art accuracy with low computational cost in single and multi-category object detection tasks.

翻译：图像信号处理器在图像识别任务以及捕获图像的感知质量中扮演着重要角色。多数情况下，专家需投入大量精力手动调节图像信号处理器的众多参数，但这些参数往往并非最优。现有文献中，两种技术得到广泛研究：基于机器学习的参数调优技术和基于深度神经网络的图像信号处理器技术。前者计算轻量但表达能力不足，后者虽具强大表达能力，但在边缘设备上计算成本过高。为解决这些问题，我们提出"DynamicISP"，该架构由多个经典图像信号处理功能模块组成，能够根据前一帧的识别结果动态调整当前帧各参数。实验表明，本方法可成功调控多个图像信号处理功能模块的参数，在单类别和多类别目标检测任务中均能以低计算成本实现当前最优精度。