Joint denoising and HDR for RAW video sequences
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 96 references that are in the map. First posted on arXiv 18 Jan 2022.
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13 of 96 references.
References · 13
- 2017Burst Denoising with Kernel Prediction Networkscited by 156
- 2019FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimationcited by 128
- 2019Attention-guided Network for Ghost-free High Dynamic Range Imagingcited by 116
- 2017Deep High Dynamic Range Imaging with Large Foreground Motionscited by 98
- 2020HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motionscited by 80
- 2019DVDnet: A Fast Network for Deep Video Denoisingcited by 60
- 2021ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imagingcited by 51
- 2021Patch Craft: Video Denoising by Deep Modeling and Patch Matchingcited by 38
- 2018Image Correction via Deep Reciprocating HDR Transformationcited by 30
- 2018Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Networkcited by 26
- 2019Deep Tone Mapping Operator for High Dynamic Range Imagescited by 26
- 2020Self-Supervised training for blind multi-frame video denoisingcited by 26
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- 2020Explorable Tone Mapping Operatorscited by 6
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