Search Results for author: Yuhui Quan

Found 25 papers, 9 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

Single Image Defocus Deblurring via Implicit Neural Inverse Kernels

1 code implementation ICCV 2023 Yuhui Quan, Xin Yao, Hui Ji

Single image defocus deblurring (SIDD) is a challenging task due to the spatially-varying nature of defocus blur, characterized by per-pixel point spread functions (PSFs).

Deblurring Image Defocus Deblurring

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

Diffuse3D: Wide-Angle 3D Photography via Bilateral Diffusion

1 code implementation ICCV 2023 Yutao Jiang, Yang Zhou, Yuan Liang, Wenxi Liu, Jianbo Jiao, Yuhui Quan, Shengfeng He

To address the above issues, we propose Diffuse3D which employs a pre-trained diffusion model for global synthesis, while amending the model to activate depth-aware inference.

Denoising Novel View Synthesis

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

Encoding Spatial Distribution of Convolutional Features for Texture Representation

1 code implementation NeurIPS 2021 Yong Xu, Feng Li, Zhile Chen, Jinxiu Liang, Yuhui Quan

Existing convolutional neural networks (CNNs) often use global average pooling (GAP) to aggregate feature maps into a single representation.

Material Recognition Retrieval +1

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.

Computational Efficiency Deblurring +2

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

Recurrent Exposure Generation for Low-Light Face Detection

1 code implementation21 Jul 2020 Jinxiu Liang, Jingwen Wang, Yuhui Quan, Tianyi Chen, Jiaying Liu, Haibin Ling, Yong Xu

REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions.

Face Detection Image Enhancement

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

Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image

1 code implementation CVPR 2020 Yuhui Quan, Mingqin Chen, Tongyao Pang, Hui Ji

In last few years, supervised deep learning has emerged as one powerful tool for image denoising, which trains a denoising network over an external dataset of noisy/clean image pairs.

Image Denoising Self-Supervised Learning

Image Cartoon-Texture Decomposition Using Isotropic Patch Recurrence

no code implementations10 Nov 2018 Ruotao Xu, Yuhui Quan, Yong Xu

Aiming at separating the cartoon and texture layers from an image, cartoon-texture decomposition approaches resort to image priors to model cartoon and texture respectively.

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.

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).

Computational Efficiency Dictionary Learning +1

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

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

Lacunarity Analysis on Image Patterns for Texture Classification

no code implementations CVPR 2014 Yuhui Quan, Yong Xu, Yuping Sun, Yu Luo

Based on the concept of lacunarity in fractal geometry, we developed a statistical approach to texture description, which yields highly discriminative feature with strong robustness to a wide range of transformations, including photometric changes and geometric changes.

Classification General Classification +1

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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