用相机参数控制去噪强度，让去噪更灵活精准。
Towards Controllable Real Image Denoising with Camera Parameters
- 用ISO、快门速度、光圈值生成控制向量调节去噪程度。
- 在真实图像上实现可调去噪，性能优于传统方法。
- 适合需要精细控制去噪效果的摄影与图像处理场景。
近期基于深度学习的图像去噪方法表现优异，但多数缺乏根据噪声水平、相机设置和用户偏好调整去噪强度的灵活性。本文提出一种新的可控去噪框架，通过利用相机参数信息自适应地去除图像噪声。重点使用与噪声水平密切相关的ISO、快门速度和光圈值，将其转换为向量以控制并增强去噪网络性能。实验结果表明，该方法能无缝提升标准去噪神经网络的可控性与性能。代码已公开于https://github.com/OBAKSA/CPADNet。
原文摘要 · Abstract (English)
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.
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