Computer Graphics Laboratory ETH Zurich


Robust Image Denoising using Kernel Predicting Networks

Z. Cai, Y. Zhang, M. Manzi, A. C. Oztireli, M. Gross, T. Aydin

Eurographics - Short Papers (Vienna, Austria, May 3-7, 2021), pp. 37-40


We present a new method for designing high quality denoisers that are robust to varying noise characteristics of input images. Instead of taking a conventional blind denoising approach or relying on explicit noise parameter estimation networks as well as invertible camera imaging pipeline models, we propose a two-stage model that first processes an input image with a small set of specialized denoisers, and then passes the resulting intermediate denoised images to a kernel predicting network that estimates per-pixel denoising kernels. We demonstrate that our approach achieves robustness to noise parameters at a level that exceeds comparable blind denoisers, while also coming close to state-of-the-art denoising quality for camera sensor noise.


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