Search Results for author: Hui Ji

Found 25 papers, 6 papers with code

Self-supervised Bayesian Deep Learning for Image Recovery with Applications to Compressive Sensing

1 code implementation ECCV 2020 Tongyao Pang, Yuhui Quan, Hui Ji

In recent years, deep learning emerges as one promising technique for solving many ill-posed inverse problems in image recovery, and most deep-learning-based solutions are based on supervised learning.

Compressive Sensing Image Reconstruction

Ground-Truth Free Meta-Learning for Deep Compressive Sampling

no code implementations CVPR 2023 Xinran Qin, Yuhui Quan, Tongyao Pang, Hui Ji

To further improve the learning on the null space of the measurement matrix, a modified model-agnostic meta-learning scheme is proposed, along with a null-space-consistent loss and a bias-adaptive deep unrolling network to improve and accelerate model adaption in test time.

Image Reconstruction Meta-Learning +1

Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization

no code implementations CVPR 2023 Ji Li, Weixi Wang, Yuesong Nan, Hui Ji

In contrast, this paper presents a dataset-free deep learning method for removing uniform and non-uniform blur effects from images of static scenes.


Synthesis of realistic fetal MRI with conditional Generative Adversarial Networks

no code implementations20 Sep 2022 Marina Fernandez Garcia, Rodrigo Gonzalez Laiz, Hui Ji, Kelly Payette, Andras Jakab

In the future, this algorithm would be used for generating large, synthetic datasets representing fetal brain development.

SSIM Super-Resolution

A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction

no code implementations1 May 2022 Qiaoqiao Ding, Hui Ji, Yuhui Quan, Xiaoqun Zhang

Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation.

Bayesian Inference Image Reconstruction

Self-Supervised Deep Image Restoration via Adaptive Stochastic Gradient Langevin Dynamics

1 code implementation CVPR 2022 Weixi Wang, Ji Li, Hui Ji

While supervised deep learning has been a prominent tool for solving many image restoration problems, there is an increasing interest on studying self-supervised or un- supervised methods to address the challenges and costs of collecting truth images.

Image Restoration Retrieval

Gaussian Kernel Mixture Network for Single Image Defocus Deblurring

1 code implementation NeurIPS 2021 Yuhui Quan, Zicong Wu, Hui Ji

Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount.

Deblurring Image Defocus Deblurring +1

Deep Texture Recognition via Exploiting Cross-Layer Statistical Self-Similarity

no code implementations CVPR 2021 Zhile Chen, Feng Li, Yuhui Quan, Yong Xu, Hui Ji

In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition.

Self-supervised Bayesian Deep Learning for Image Denoising

no code implementations1 Jan 2021 Tongyao Pang, Yuhui Quan, Hui Ji

Built on the Bayesian neural network (BNN), this paper proposed a self-supervised deep learning method for denoising a single image, in the absence of training samples.

Image Denoising

Deep Bilateral Retinex for Low-Light Image Enhancement

no code implementations4 Jul 2020 Jinxiu Liang, Yong Xu, Yuhui Quan, Jingwen Wang, Haibin Ling, Hui Ji

Low-light images, i. e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise.

Low-Light Image Enhancement

Convolutional Neural Network on Semi-Regular Triangulated Meshes and its Application to Brain Image Data

no code implementations21 Mar 2019 Caoqiang Liu, Hui Ji, Anqi Qiu

We developed a convolution neural network (CNN) on semi-regular triangulated meshes whose vertices have 6 neighbours.

Weighted total variation based convex clustering

no code implementations28 Aug 2018 Guodong Xu, Yu Xia, Hui Ji

Data clustering is a fundamental problem with a wide range of applications.

Removing out-of-focus blur from a single image

no code implementations28 Aug 2018 Guodong Xu, Chaoqiang Liu, Hui Ji

Reproducing an all-in-focus image from an image with defocus regions is of practical value in many applications, eg, digital photography, and robotics.

Estimating Defocus Blur via Rank of Local Patches

no code implementations ICCV 2017 Guodong Xu, Yuhui Quan, Hui Ji

This paper addresses the problem of defocus map estimation from a single image.

Sparse Coding for Classification via Discrimination Ensemble

no code implementations CVPR 2016 Yuhui Quan, Yong Xu, Yuping Sun, Yan Huang, Hui Ji

Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminability of sparse codes.

Classification Dictionary Learning +1

Equiangular Kernel Dictionary Learning With Applications to Dynamic Texture Analysis

no code implementations CVPR 2016 Yuhui Quan, Chenglong Bao, Hui Ji

Most existing dictionary learning algorithms consider a linear sparse model, which often cannot effectively characterize the nonlinear properties present in many types of visual data, e. g. dynamic texture (DT).

Dictionary Learning Texture Classification

Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning

no code implementations ICCV 2015 Yuhui Quan, Yan Huang, Hui Ji

In addition, based on the proposed dictionary learning method, a DT descriptor is developed, which has better adaptivity, discriminability and scalability than the existing approaches.

Dictionary Learning Dynamic Texture Recognition

Removing Rain From a Single Image via Discriminative Sparse Coding

no code implementations ICCV 2015 Yu Luo, Yong Xu, Hui Ji

The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i. e. separate the rain layer and the de-rained image layer from an rainy image.

Dictionary Learning Rain Removal

l0 Norm Based Dictionary Learning by Proximal Methods with Global Convergence

no code implementations CVPR 2014 Chenglong Bao, Hui Ji, Yuhui Quan, Zuowei Shen

Sparse coding and dictionary learning have seen their applications in many vision tasks, which usually is formulated as a non-convex optimization problem.

Dictionary Learning Face Recognition

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