# Efficient joint noise removal and multi exposure fusion

arXiv: https://arxiv.org/abs/2112.03701 · HTML: https://mlsanity.com/paper/2112.03701/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 78 references that are in the map.
- First posted on arXiv 4 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Deep High Dynamic Range Imaging with Large Foreground Motions](https://mlsanity.com/paper/1711.08937/) (2017)
- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [Perceptually Optimized Image Rendering](https://mlsanity.com/paper/1701.06641/) (2017)
- [Removing Camera Shake via Weighted Fourier Burst Accumulation](https://mlsanity.com/paper/1505.02731/) (2015)
- [Hand-held Video Deblurring via Efficient Fourier Aggregation](https://mlsanity.com/paper/1509.05251/) (2015)

## Abstract (arXiv)

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.