398 citations · 537 across the 28 of their papers we have counts for
28 papers
Flash EQ-Linear: Accelerating Equivariant Linear Layers via Group-wise Discrete Fourier Transform
Zhongchen Zhao, Jixin Wang, Qi Xie +4
Equivariant networks embed geometric symmetries as structural priors through weight sharing, achieving remarkable parameter efficiency across vision tasks. However, this parameter…
Aligning Network Equivariance with Data Symmetry: A Theoretical Framework and Adaptive Approach for Image Restoration
Feiyu Tan, Qi Xie, Zongben Xu +1
Image restoration is an inherently ill posed inverse problem. Equivariant networks that embed geometric symmetry priors can mitigate this ill posedness and improve performance. How…
Rotation Equivariant Mamba for Vision Tasks
Zhongchen Zhao, Qi Xie, Keyu Huang +3
Rotation equivariance constitutes one of the most general and crucial structural priors for visual data, yet it remains notably absent from current Mamba-based vision architectures…
Vanilla Group Equivariant Vision Transformer: Simple and Effective
Jiahong Fu, Qi Xie, Deyu Meng +1
Incorporating symmetry priors as inductive biases to design equivariant Vision Transformers (ViTs) has emerged as a promising avenue for enhancing their performance. However, exist…
Rotation Equivariant Arbitrary-scale Image Super-Resolution
Qi Xie, Jiahong Fu, Zongben Xu +1
The arbitrary-scale image super-resolution (ASISR), a recent popular topic in computer vision, aims to achieve arbitrary-scale high-resolution recoveries from a low-resolution inpu…
DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction
Yuliang Zhu, Jing Cheng, Qi Xie +7
Dynamic Magnetic Resonance Imaging (MRI) exhibits transformation symmetries, including spatial rotation symmetry within individual frames and temporal symmetry along the time dimen…
A Regularization-Guided Equivariant Approach for Image Restoration
Yulu Bai, Jiahong Fu, Qi Xie +1
Equivariant and invariant deep learning models have been developed to exploit intrinsic symmetries in data, demonstrating significant effectiveness in certain scenarios. However, t…
Rotation-Equivariant Self-Supervised Method in Image Denoising
Hanze Liu, Jiahong Fu, Qi Xie +1
Self-supervised image denoising methods have garnered significant research attention in recent years, for this kind of method reduces the requirement of large training datasets. Co…
Continuous Filtered Backprojection by Learnable Interpolation Network
Hui Lin, Dong Zeng, Qi Xie +3
Accurate reconstruction of computed tomography (CT) images is crucial in medical imaging field. However, there are unavoidable interpolation errors in the backprojection step of th…
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution
Xinyi Liu, Feiyu Tan, Qi Xie +2
Burst image processing (BIP), which captures and integrates multiple frames into a single high-quality image, is widely used in consumer cameras. As a typical BIP task, Burst Image…
TRG-Net: An Interpretable and Controllable Rain Generator
Zhiqiang Pang, Hong Wang, Qi Xie +2
Exploring and modeling rain generation mechanism is critical for augmenting paired data to ease training of rainy image processing models. Against this task, this study proposes a…
Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration
Jiahong Fu, Qi Xie, Deyu Meng +1
The deep unfolding approach has attracted significant attention in computer vision tasks, which well connects conventional image processing modeling manners with more recent deep l…
RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation
Zihong Sun, Hong Wang, Qi Xie +2
Retinal vessel segmentation is of great clinical significance for the diagnosis of many eye-related diseases, but it is still a formidable challenge due to the intricate vascular m…
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
Hong Wang, Qi Xie, Yuexiang Li +3
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical trea…
A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback
Zhengyang Hu, Guanzhang Liu, Qi Xie +3
Channel state information (CSI) plays a critical role in achieving the potential benefits of massive multiple input multiple output (MIMO) systems. In frequency division duplex (FD…
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…
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…
Learning to Purify Noisy Labels via Meta Soft Label Corrector
Yichen Wu, Jun Shu, Qi Xie +2
Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by designing a m…
From Rain Generation to Rain Removal
Hong Wang, Zongsheng Yue, Qi Xie +3
For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets. Most o…
Structural Residual Learning for Single Image Rain Removal
Hong Wang, Yichen Wu, Qi Xie +3
To alleviate the adverse effect of rain streaks in image processing tasks, CNN-based single image rain removal methods have been recently proposed. However, the performance of thes…
A Model-driven Deep Neural Network for Single Image Rain Removal
Hong Wang, Qi Xie, Qian Zhao +1
Deep learning (DL) methods have achieved state-of-the-art performance in the task of single image rain removal. Most of current DL architectures, however, are still lack of suffici…
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…