标题： 面向具有相机参数的可控制真实图像去噪 显示英文标题

标题： Towards Controllable Real Image Denoising with Camera Parameters

摘要： 基于深度学习的图像去噪方法最近表现出色；然而，许多方法缺乏根据噪声水平、相机设置和用户偏好调整去噪强度的灵活性。 在本文中，我们引入了一个新的可控去噪框架，通过利用相机参数的信息来自适应地从图像中去除噪声。 具体来说，我们关注与噪声水平密切相关的ISO、快门速度和F数。 我们将这些选定的参数转换为一个向量，以控制并增强去噪网络的性能。 实验结果表明，我们的方法无缝地为标准去噪神经网络增加了可控性并提高了它们的性能。 代码可在 https://github.com/OBAKSA/CPADNet 获取。 显示英文摘要

摘要： Recent deep learning-based image denoising methods have shown impressive performance; however, many lack the flexibility to adjust the denoising strength based on the noise levels, camera settings, and user preferences. In this paper, we introduce a new controllable denoising framework that adaptively removes noise from images by utilizing information from camera parameters. Specifically, we focus on ISO, shutter speed, and F-number, which are closely related to noise levels. We convert these selected parameters into a vector to control and enhance the performance of the denoising network. Experimental results show that our method seamlessly adds controllability to standard denoising neural networks and improves their performance. Code is available at https://github.com/OBAKSA/CPADNet.