Denoising scheme for realistic digital photos from unknown sources

This paper targets denoising of digital photos taken by cameras with unknown sensor parameters and image processing pipeline. Common noise characteristics in such images originate from camera-internal processing, such as demosaicing, tone mapping, and JPEG compression. Three of the noise characteristics that are not adequately addressed by existing denoising algorithms are spatially correlated low-frequency noise, strong signal dependency of the noise level and high levels of the chrominance noise relative to the luminance noise. We propose a generic scheme that extends existing denoisers such as the bilateral filter to account for all the problems above. Our solution combines a novel progressive pyramidal filtering scheme to address the correlated noise, filter adaptation via local noise level estimation and luminance-guided chrominance filtering to address the low-SNR of the chrominance channels. We demonstrate the effectiveness of our solution for challenging realistic noisy photos.