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.
no code implementations • CVPR 2023 • Yuhui Quan, Zicong Wu, Hui Ji
Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one.
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.
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.
no code implementations • 20 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.
1 code implementation • IEEE Transactions on Circuits and Systems for Video Technology 2022 • Jinxiu Liang, Yong Xu, Yuhui Quan, Boxin Shi, Hui Ji
The enhancement is done by jointly optimizing the Retinex decomposition and the illumination adjustment.
no code implementations • 1 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.
no code implementations • 20 Apr 2022 • Kelly Payette, Hongwei Li, Priscille de Dumast, Roxane Licandro, Hui Ji, Md Mahfuzur Rahman Siddiquee, Daguang Xu, Andriy Myronenko, Hao liu, Yuchen Pei, Lisheng Wang, Ying Peng, Juanying Xie, Huiquan Zhang, Guiming Dong, Hao Fu, Guotai Wang, ZunHyan Rieu, Donghyeon Kim, Hyun Gi Kim, Davood Karimi, Ali Gholipour, Helena R. Torres, Bruno Oliveira, João L. Vilaça, Yang Lin, Netanell Avisdris, Ori Ben-Zvi, Dafna Ben Bashat, Lucas Fidon, Michael Aertsen, Tom Vercauteren, Daniel Sobotka, Georg Langs, Mireia Alenyà, Maria Inmaculada Villanueva, Oscar Camara, Bella Specktor Fadida, Leo Joskowicz, Liao Weibin, Lv Yi, Li Xuesong, Moona Mazher, Abdul Qayyum, Domenec Puig, Hamza Kebiri, Zelin Zhang, Xinyi Xu, Dan Wu, Kuanlun Liao, Yixuan Wu, Jintai Chen, Yunzhi Xu, Li Zhao, Lana Vasung, Bjoern Menze, Meritxell Bach Cuadra, Andras Jakab
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
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.
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.
1 code implementation • CVPR 2021 • Tongyao Pang, Huan Zheng, Yuhui Quan, Hui Ji
Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising.
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.
no code implementations • 1 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.
1 code implementation • 29 Oct 2020 • Kelly Payette, Priscille de Dumast, Hamza Kebiri, Ivan Ezhov, Johannes C. Paetzold, Suprosanna Shit, Asim Iqbal, Romesa Khan, Raimund Kottke, Patrice Grehten, Hui Ji, Levente Lanczi, Marianna Nagy, Monika Beresova, Thi Dao Nguyen, Giancarlo Natalucci, Theofanis Karayannis, Bjoern Menze, Meritxell Bach Cuadra, Andras Jakab
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
no code implementations • 4 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.
no code implementations • 16 Dec 2019 • Jiulong Liu, Angelica I. Aviles-Rivero, Hui Ji, Carola-Bibiane Schönlieb
We also introduce registration blocks based deep nets to predict the registration parameters and warp transformation accurately and efficiently.
no code implementations • 21 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.
no code implementations • 28 Aug 2018 • Guodong Xu, Yu Xia, Hui Ji
Data clustering is a fundamental problem with a wide range of applications.
no code implementations • 28 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.
no code implementations • ICCV 2017 • Guodong Xu, Yuhui Quan, Hui Ji
This paper addresses the problem of defocus map estimation from a single image.
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.
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).
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.
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.
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.