computer-vision-1

Multi-Exposure Image Fusion

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Dual Illumination Estimation for Robust Exposure Correction

Qing Zhang, Weishi Zheng, Yongwei Nie•Mon Sep 30 2019

This paper presents a novel automatic exposure correction method, able to robustly produce high‐quality results for images of various exposure conditions, and demonstrates the effectiveness of the proposed approach and its superiority over the state‐of‐the‐art methods and popularautomatic exposure correction tools.

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Deep Convolutional Sparse Coding Networks for Image Fusion

Yicheng Wang, Shuang Xu, Jiangshe Zhang, Chunxia Zhang, Zixiang Zhao, Junmin Liu•Sun May 17 2020

Three deep convolutional sparse coding networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion and multi-modal image fusion) are presented.

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TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning

Shaolei Liu, Manning Wang, Linhao Qu, Zhijian Song•Wed Dec 01 2021

This paper proposes TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning that is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images.

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Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion

Jinyuan Liu, Pan Mu, Zhiying Du, Cong Bai•Wed Oct 25 2023

This study proposes a gamma correction module specifically designed to fully leverage latent information embedded within source images, and a novel color enhancement algorithm is presented to augment image saturation while preserving intricate details.

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Ghost-free multi exposure image fusion technique using dense SIFT descriptor and guided filter

Naila Hayat, M. Imran•Sun Jun 30 2019

Experimental results prove the superiority of the proposed technique over existing state-of-the-art methods in terms of both subjective and objective evaluation.

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FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization

S. Gumhold, Nishant Kumar•Sat Dec 05 2020

A novel real-time visualization tool, named FuseVis, with which the end-user can compute per-pixel saliency maps that examine the influence of the input image pixels on each pixel of the fused image.

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Pas-Mef: Multi-Exposure Image Fusion Based On Principal Component Analysis, Adaptive Well-Exposedness And Saliency Map

Diclehan Karakaya, Oguzhan Ulucan, Mehmet Türkan•Mon May 24 2021

This study proposes an efficient multi-exposure fusion (MEF) approach with a simple yet effective weight extraction method relying on principal component analysis, adaptive well-exposedness and saliency maps.

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Cross Attention-guided Dense Network for Images Fusion

Zhengwen Shen, Jun Wang, Zaiyu Pan, Yulian Li, Jiang Wang•Wed Sep 22 2021

A novel cross-attention-guided image fusion network is proposed, which is a unified and unsupervised framework for multi-modal image fusion, multi-exposure image fused, and multi-focus image fusion.

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Perceptual Multi-Exposure Fusion

Xiaoning Liu•Mon Oct 17 2022

A perceptual multi-exposure fusion method that not just ensures fine shadow/highlight details but with lower complexity than detailenhanced methods, and can achieve a better improvement for current image enhancement techniques, ensuring fine detail in bright light.

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Embracing Compact and Robust Architectures for Multi-Exposure Image Fusion

Jinyuan Liu, Zhu Liu, Guanyao Wu, Xin-Yue Fan, Risheng Liu•Sat Dec 31 2022

This paper proposes a search-based paradigm, involving self-alignment and detail repletion modules for robust multi-exposure image fusion, and introduces neural architecture search to discover compact and efﬁcient networks, investigating effective feature representation for fusion.

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