Multi-Exposure Image Fusion
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Exposure Image Fusion
Most implemented papers
Dual Illumination Estimation for Robust Exposure Correction
By performing dual illumination estimation, we obtain two intermediate exposure correction results for the input image, with one fixes the underexposed regions and the other one restores the overexposed regions.
Deep Convolutional Sparse Coding Networks for Image Fusion
xsxjtu/CSC-MEFN
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Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few.
TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning
miccaiif/Official-PyTorch-Implementation-of-TransMEF
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In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning.
Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion
zhiyingdu/bhfmef
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In recent years, deep learning networks have made remarkable strides in the domain of multi-exposure image fusion.
Ghost-free multi exposure image fusion technique using dense SIFT descriptor and guided filter
A ghost-free multi-exposure image fusion technique using the dense SIFT descriptor and the guided filter is proposed in this paper.
FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization
nish03/FuseVis
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However, it is challenging to analyze the reliability of these CNNs for the image fusion tasks since no groundtruth is available.
PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map
High dynamic range (HDR) imaging enables to immortalize natural scenes similar to the way that they are perceived by human observers.
Cross Attention-guided Dense Network for Images Fusion
shenzw21/cadnif
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In this paper, we propose a novel cross-attention-guided image fusion network, which is a unified and unsupervised framework for multi-modal image fusion, multi-exposure image fusion, and multi-focus image fusion.
Perceptual Multi-Exposure Fusion
Experiments on the constructed dataset demonstrate that the proposed method exceeds existing eight state-of-the-art approaches in terms of visually and MEF-SSIM value.
Embracing Compact and Robust Architectures for Multi-Exposure Image Fusion
liuzhu-cv/crmef
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In recent years, deep learning-based methods have achieved remarkable progress in multi-exposure image fusion.
