Efficient joint noise removal and multi exposure fusion

TL;DR

This paper introduces an efficient joint noise removal and multi-exposure image fusion method that leverages DCT processing and collaborative 3D thresholding to improve image quality without reconstructing individual denoised images.

Contribution

A novel multi-exposure fusion approach that simultaneously denoises and fuses images using spatio-temporal patch selection and collaborative 3D thresholding, enhancing efficiency.

Findings

Effective noise removal during fusion process

Improved image quality without separate denoising step

Reduced computational complexity

Abstract

Multi-exposure fusion (MEF) is a technique for combining different images of the same scene acquired with different exposure settings into a single image. All the proposed MEF algorithms combine the set of images, somehow choosing from each one the part with better exposure. We propose a novel multi-exposure image fusion chain taking into account noise removal. The novel method takes advantage of DCT processing and the multi-image nature of the MEF problem. We propose a joint fusion and denoising strategy taking advantage of spatio-temporal patch selection and collaborative 3D thresholding. The overall strategy permits to denoise and fuse the set of images without the need of recovering each denoised exposure image, leading to a very efficient procedure.