dlut-dimt/FKDA

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MATLAB
Created about 5 years ago
Updated about 5 years ago

dlut-dimt/FKDA

dlut-dimt

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Source code for our IEEE TPAMI paper: Yi Wang, Yi Ding, Xiangjian He, Xin Fan*, Chi Lin, Fengqi Li, Tianzhu Wang, Zhongxuan Luo, Jiebo Luo, “Novelty Detection and Online Learning for Chunk Data Streams”, IEEE TPAMI, 2019. (DOI: 10.1109/TPAMI.2020.2965531)

Realworld-Underwater-Image-Enhancement-RUIE-Benchmark

Paper “Real-world Underwater Enhancement: Challenging, Benchmark and Efficient Solutions” https://arxiv.org/abs/1901.05320

ellipse-detector

LineMatching

Line matching code of ECCV2016

ReCoNet

ECCV 2022 | Recurrent Correction Network for Fast and Efficient Multi-modality Image Fusion.

SegMaR

Underwater-image-enhancement-algorithms

RUOD

Two-Layer-GPR-Dehazing

The source code of Two-layer Gaussian Process Regression with Example Selection for Image Dehazing, TCSVT

HCNCCode

Source codes of "Hierarchical Projective Invariant Contexts for Shape Recognition"

TGDOF

# TGDOF This is the testing code of TGDOF for CS-MRI. Running the script "AddPath" and then the "Demo_TGDOF" to test the basic deep framework for CS-MRI. TestData ------------ The testing MR slices used in experiments, including 25 T1-weighted data and 25 T2-weighted data. The slices are extracted from the subset of the IXI datasets: http://brain-development.org/ixi-dataset/ ArtifactsModel ------------ The pre-trained model used in Module \mathcal{N}. SamplingPatter: ------------ The three kinds of sampling patterns at five different sampling ratios (10% to 50%). If you utilize this code, please cite the related paper: <br> @inproceedings{liu2019theoretically,<br> title={A theoretically guaranteed deep optimization framework for robust compressive sensing mri},<br> author={Liu, Risheng and Zhang, Yuxi and Cheng, Shichao and Fan, Xin and Luo, Zhongxuan},<br> booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},<br> volume={33},<br> pages={4368--4375},<br> year={2019} }

PODM

A Bridging Framework for Model Optimization and Deep Propagation (NIPS-2018)

DPE-Deep-Prior-Ensemble

The source code of paper “Learning Converged Propagations with Deep Prior Ensemble for Image Enhancement”

Shape-to-gradient-regression

An implementation of Shape-to-gradient regression in "Explicit Shape Regression with Characteristic Number for Facial Landmark Localization "