Search Results for author: Liang-Jian Deng

Found 7 papers, 2 papers with code

LAConv: Local Adaptive Convolution for Image Fusion

no code implementations24 Jul 2021 Zi-Rong Jin, Liang-Jian Deng, Tai-Xiang Jiang, Tian-Jing Zhang

The convolution operation is a powerful tool for feature extraction and plays a prominent role in the field of computer vision.

Hyperspectral Image Super-Resolution Image Super-Resolution

Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks

no code implementations29 May 2020 Jin-Fan Hu, Ting-Zhu Huang, Liang-Jian Deng, Tai-Xiang Jiang, Gemine Vivone, Jocelyn Chanussot

In order to alleviate this issue, in this work, we propose a simple and efficient architecture for deep convolutional neural networks to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution hyperspectral image (HR-HSI).

Hyperspectral Image Super-Resolution Image Super-Resolution

Rain Streak Removal for Single Image via Kernel Guided CNN

no code implementations26 Aug 2018 Ye-Tao Wang, Xi-Le Zhao, Tai-Xiang Jiang, Liang-Jian Deng, Yi Chang, Ting-Zhu Huang

Then, our framework starts with learning the motion blur kernel, which is determined by two factors including angle and length, by a plain neural network, denoted as parameter net, from a patch of the texture component.

FastDeRain: A Novel Video Rain Streak Removal Method Using Directional Gradient Priors

3 code implementations20 Mar 2018 Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng, Yao Wang

In this paper, we propose a novel video rain streak removal approach FastDeRain, which fully considers the discriminative characteristics of rain streaks and the clean video in the gradient domain.

Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm

5 code implementations15 Dec 2017 Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng

In this paper, we investigate tensor recovery problems within the tensor singular value decomposition (t-SVD) framework.

Single image super-resolution by approximated Heaviside functions

no code implementations12 Mar 2015 Liang-Jian Deng, Weihong Guo, Ting-Zhu Huang

We propose a new iterative model for single image super-resolution based on an observation: an image is consisted of smooth components and non-smooth components, and we use two classes of approximated Heaviside functions (AHFs) to represent them respectively.

Image Super-Resolution

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