Joint Denoising and HDR for RAW Image Sequences

Abstract

[eng] We propose a patch-based method for the simultaneous denoising and fusion of a sequence of multi-exposed RAW images. A spatio-temporal criterion is used to select similar patches along the sequence, and a weighted principal component analysis (WPCA) simultaneously denoises and fuses the multi-exposed data. The overall strategy permits to denoise and fuse the set of images without the need to recover each denoised image in the multi-exposure set, leading to a very efficient procedure. Moreover, ghosting removal is included naturally as part of the method by {the} way patches are selected and the weighted principal component analysis. Several experiments show that the proposed method obtains state-of-the-art fusion results with real~RAW~data. The method is very flexible, it can be easily adapted to other kinds of noise and extended to video HDR and denoising.