Towards Controllable Real Image Denoising with Camera Parameters
This addresses the need for adaptable denoising in photography and imaging applications, though it is incremental as it builds on existing neural networks.
The paper tackles the problem of inflexible image denoising by introducing a controllable framework that uses camera parameters like ISO, shutter speed, and F-number to adjust denoising strength, improving performance without specifying concrete numbers.
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.