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WUHAN UNIVERSITY
- WUHAN CHINA
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[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
Repository of DELIVER dataset and CMNeXt models (CVPR 2023)
Official implementation of the paper "Complementary Random Masking for RGB-T Semantic Segmentation."
[TCSVT2023] [LASNet] RGB-T Semantic Segmentation with Location, Activation, and Sharpening
Spatial-Spectral Quasi-Attention Recurrent Network for Hyperspectral Image Denoising.
Fall Detection Based on Skeletal Image(2018.04)
Spectral-Spatial Attention Network for Hyperspectral Image Classification
Code of P2Sharpen: A progressive pansharpening network with deep spectral transformation.
IRLF-WHU dataset in Progressive Fusion Network Based on Infrared Light Field Equipment for Infrared Image Enhancement JAS2022
Deep Learning-based Image Fusion: A Survey
The pytorch implementation of RetinexDIP, a unified zero-reference deep framework for low-light enhancement.
Codes and models - "SDGMNet: Statistic-based Dynamic Gradient Modulation for Local Descriptor Learning"
Code of "Cross Fusion Net: A Fast Semantic Segmentation Network for Small-scale Semantic Information Capturing in Aerial Scenes"
Code of "DBDnet: A Deep Boosting Strategy for Image Denoising"
Kindling the Darkness: a Practical Low-light Image Enhancer
The MATLAB code of "Appearance-based Loop Closure Detection via Locality-driven Accurate Motion Field Learning"
The MATLAB code of "Appearance-based Loop Closure Detection via Bidirectional Manifold Representation Consensus"
A dataset for the segmentation of the wheat stalk cross section micrographs
Code of FusionDN (AAAI 2020): A Unified Densely Connected Network for Image Fusion
Code of DDcGAN for infrared and visible image fusion and medical image fusion
Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.
The code of "Infrared and visible image fusion via detail preserving adversarial learning"