122 citations · 294 across the 10 of their papers we have counts for
7 papers · 1 filter
Hierarchical Integration Diffusion Model for Realistic Image Deblurring
Zheng Chen, Yulun Zhang, Ding Liu +4
Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusio…
Binarized Spectral Compressive Imaging
Yuanhao Cai, Yuxin Zheng, Jing Lin +3
Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources.…
Universal Domain Adaptation for Remote Sensing Image Scene Classification
Qingsong Xu, Yilei Shi, Xin Yuan +1
The domain adaptation (DA) approaches available to date are usually not well suited for practical DA scenarios of remote sensing image classification, since these methods (such as…
Cross Aggregation Transformer for Image Restoration
Zheng Chen, Yulun Zhang, Jinjin Gu +3
Recently, Transformer architecture has been introduced into image restoration to replace convolution neural network (CNN) with surprising results. Considering the high computationa…
Accurate Image Restoration with Attention Retractable Transformer
Jiale Zhang, Yulun Zhang, Jinjin Gu +3
Recently, Transformer-based image restoration networks have achieved promising improvements over convolutional neural networks due to parameter-independent global interactions. To…
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
Yuanhao Cai, Jing Lin, Xiaowan Hu +5
Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i.e., recovering the 3D hyperspectral images (HSIs) from a 2D…
Spatial--spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images
Qingsong Xu, Xin Yuan, Chaojun Ouyang +1
We consider the problem of segmentation and classification of high-resolution and hyperspectral remote sensing images. Unlike conventional natural (RGB) images, the inherent large…