Joint Denoising and HDR for RAW Image Sequences
Antoni Ramos Buades, Onofre Martorell, M. Sánchez-Beeckman · IEEE Transactions on Computational Imaging · 2024
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.