398 citations · 931 across the 31 of their papers we have counts for
31 papers
Layout-Guided Controllable Pathology Image Generation with In-Context Diffusion Transformers
Yuntao Shou, Xiangyong Cao, Qian Zhao +1
Controllable pathology image synthesis requires reliable regulation of spatial layout, tissue morphology, and semantic detail. However, existing text-guided diffusion models offer…
Enhancing Underwater Light Field Images via Global Geometry-aware Diffusion Process
Yuji Lin, Qian Zhao, Zongsheng Yue +2
This work studies the challenging problem of acquiring high-quality underwater images via 4-D light field (LF) imaging. To this end, we propose GeoDiff-LF, a novel diffusion-based…
Generative Latent Kernel Modeling for Blind Motion Deblurring
Chenhao Ding, Jiangtao Zhang, Zongsheng Yue +3
Deep prior-based approaches have demonstrated remarkable success in blind motion deblurring (BMD) recently. These methods, however, are often limited by the high non-convexity of t…
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction
Yuntao Shou, Peiqiang Yan, Xingjian Yuan +3
In recent years, histopathological whole slide image (WSI)- based survival analysis has attracted much attention in medical image analysis. In practice, WSIs usually come from diff…
Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method
Yuji Lin, Junhui Hou, Xianqiang Lyu +2
In this paper, we delve into the realm of 4-D light fields (LFs) to enhance underwater imaging plagued by light absorption, scattering, and other challenges. Contrasting with conve…
Blind Image Deconvolution by Generative-based Kernel Prior and Initializer via Latent Encoding
Jiangtao Zhang, Zongsheng Yue, Hui Wang +2
Blind image deconvolution (BID) is a classic yet challenging problem in the field of image processing. Recent advances in deep image prior (DIP) have motivated a series of DIP-base…
Interactive Segmentation as Gaussian Process Classification
Minghao Zhou, Hong Wang, Qian Zhao +4
Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly foll…
A Data-driven Loss Weighting Scheme across Heterogeneous Tasks for Image Denoising
Xiangyu Rui, Xiangyong Cao, Xile Zhao +2
In a variational denoising model, weight in the data fidelity term plays the role of enhancing the noise-removal capability. It is profoundly correlated with noise information, whi…
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Jiahong Fu, Hong Wang, Qi Xie +3
Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically co…
Diagnosing Batch Normalization in Class Incremental Learning
Minghao Zhou, Quanziang Wang, Jun Shu +2
Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL). As building blocks of DNNs, batch normalization (BN) standardizes intermedia…
Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment
Xinyi Liu, Qi Xie, Qian Zhao +2
Motivated by their recent advances, deep learning techniques have been widely applied to low-light image enhancement (LIE) problem. Among which, Retinex theory based ones, mostly f…
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
Qi Xie, Qian Zhao, Zongben Xu +1
It has been shown that equivariant convolution is very helpful for many types of computer vision tasks. Recently, the 2D filter parametrization technique plays an important role wh…
RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining
Hong Wang, Qi Xie, Qian Zhao +4
As a common weather, rain streaks adversely degrade the image quality. Hence, removing rains from an image has become an important issue in the field. To handle such an ill-posed s…
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
Zongsheng Yue, Qian Zhao, Jianwen Xie +3
While researches on model-based blind single image super-resolution (SISR) have achieved tremendous successes recently, most of them do not consider the image degradation sufficien…
Deep Variational Network Toward Blind Image Restoration
Zongsheng Yue, Hongwei Yong, Qian Zhao +3
Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two differe…
Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding
Minghan Li, Xiangyong Cao, Qian Zhao +3
Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of…
Variational Denoising Network: Toward Blind Noise Modeling and Removal
Zongsheng Yue, Hongwei Yong, Qian Zhao +2
Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new varia…
Variational Bayes' method for functions with applications to some inverse problems
Junxiong Jia, Qian Zhao, Zongben Xu +2
Bayesian approach, as a useful tool for quantifying uncertainties, has been widely used for solving inverse problems of partial differential equations (PDEs). One of the key diffic…
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Jun Shu, Qi Xie, Lixuan Yi +4
Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to alleviate…
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Qi Xie, Minghao Zhou, Qian Zhao +3
Hyperspectral imaging can help better understand the characteristics of different materials, compared with traditional image systems. However, only high-resolution multispectral (H…
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing
Jiangjun Peng, Qi Xie, Qian Zhao +3
The 3-D total variation (3DTV) is a powerful regularization term, which encodes the local smoothness prior structure underlying a hyper-spectral image (HSI), for general HSI proces…
Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement
Mingrui Geng, Yun Deng, Qian Zhao +4
Recently, deep learning(DL) methods have been proposed for the low-dose computed tomography(LdCT) enhancement, and obtain good trade-off between computational efficiency and image…
Semi-supervised Transfer Learning for Image Rain Removal
Wei Wei, Deyu Meng, Qian Zhao +2
Single image rain removal is a typical inverse problem in computer vision. The deep learning technique has been verified to be effective for this task and achieved state-of-the-art…
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Yao Wang, Jiangjun Peng, Qian Zhao +3
Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, and…