Cai, ZhilinZhang, YangManzi, MarcoOztireli, CengizGross, MarkusAydin, Tunç OzanTheisel, Holger and Wimmer, Michael2021-04-092021-04-092021978-3-03868-133-51017-4656https://doi.org/10.2312/egs.20211018https://diglib.eg.org:443/handle/10.2312/egs20211018We 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.Computing methodologiesImage processing10.2312/egs.2021101837-40