Joint denoising and HDR for RAW video sequences

TL;DR

This paper introduces a patch-based method that simultaneously denoises and fuses RAW video sequences with multi-exposure images, improving efficiency and achieving state-of-the-art results.

Contribution

It presents a novel joint denoising and HDR fusion technique that does not require recovering individual denoised images, enhancing efficiency and performance.

Findings

Achieves state-of-the-art fusion quality on real RAW data

Efficient joint denoising and HDR fusion without intermediate image recovery

Uses spatio-temporal patch selection and weighted PCA for processing

Abstract

We propose a patch-based method for the simultaneous denoising and fusion of a sequence of RAW multi-exposed images. A spatio-temporal criterion is used to select similar patches along the sequence, and a weighted principal component analysis permits to both denoise and fuse the multi exposed data. The overall strategy permits to denoise and fuse the set of images without the need of recovering each denoised image in the multi-exposure set, leading to a very efficient procedure. Several experiments show that the proposed method permits to obtain state-of-the-art fusion results with real RAW data.