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Multiscale Integration Network With Quaternion Convolution for Pansharpening
Yingjie Kong;Xuquan Wang;Kai Zhang;Hong Li;Wenbo Wan;Jiande Sun
In this letter, we proposed a multiscale integration network with quaternion convolution (MQ-Net) for the fusion of low spatial resolution multispectral (LRMS) and panchromatic (PAN) images. In this network, LRMS and PAN images are resampled at different scales and fed into feature fusion modules (FFMs) to merge the spatial and spectral information among them. Then, multiscale feature enhancement ...Show More
Texture-Content Dual Guided Network for Visible and Infrared Image Fusion
Kai Zhang;Ludan Sun;Jun Yan;Wenbo Wan;Jiande Sun;Shuyuan Yang;Huaxiang Zhang
IEEE Transactions on Multimedia
Year: 2024 | Early Access Article |
The preservation and enhancement of texture information is crucial for the fusion of visible and infrared images. However, most current deep neural network (DNN)-based methods ignore the differences between texture and content, leading to unsatisfactory fusion results. To further enhance the quality of fused images, we propose a texture-content dual guided (TCDG-Net) network, which produces the fu...Show More
Efficient Image Harmonization via RGB Transformation
Cheng Su;Jiande Sun;Jinhui Wang;Wenbo Wan;Kai Zhang;Jian Wang
2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)
Year: 2024 | Conference Paper |
Image harmonization aims to adjust the appearance of the foreground to make it harmonious with the background, thereby maintaining visual consistency in composite images. Previous deep learning-based methods have mainly focused on reconstructing harmonized images with the same size as the input composite images, often leading to complex network structures and a large number of parameters. In this ...Show More
CrossDiff: Exploring Self-SupervisedRepresentation of Pansharpening via Cross-Predictive Diffusion Model
Yinghui Xing;Litao Qu;Shizhou Zhang;Kai Zhang;Yanning Zhang;Lorenzo Bruzzone
Fusion of a panchromatic (PAN) image and corresponding multispectral (MS) image is also known as pansharpening, which aims to combine abundant spatial details of PAN and spectral information of MS images. Due to the absence of high-resolution MS images, available deep-learning-based methods usually follow the paradigm of training at reduced resolution and testing at both reduced and full resolutio...Show More
Building Change Detection in Earthquake: A Multiscale Interaction Network With Offset Calibration and a Dataset
Yunlong Liu;Kai Zhang;Chunan Guan;Shanxin Zhang;Hong Li;Wenbo Wan;Jiande Sun
As one of the most destructive natural disasters, earthquakes have struck many countries around the world in recent years, causing serious economic losses. Change detection (CD) can be applied to postearthquake building CD as it can infer interested change regions from multitemporal remote sensing (RS) images. Furthermore, the CD with short imaging intervals will better satisfy the needs of the em...Show More
VRT: A Video Restoration Transformer
Jingyun Liang;Jiezhang Cao;Yuchen Fan;Kai Zhang;Rakesh Ranjan;Yawei Li;Radu Timofte;Luc Van Gool
Video restoration aims to restore high-quality frames from low-quality frames. Different from single image restoration, video restoration generally requires to utilize temporal information from multiple adjacent but usually misaligned video frames. Existing deep methods generally tackle with this by exploiting a sliding window strategy or a recurrent architecture, which are restricted by frame-by-...Show More
Joint Spatio-Temporal Modeling for Semantic Change Detection in Remote Sensing Images
Lei Ding;Jing Zhang;Haitao Guo;Kai Zhang;Bing Liu;Lorenzo Bruzzone
Semantic change detection (SCD) refers to the task of simultaneously extracting changed areas and their semantic categories (before and after the changes) in remote sensing images (RSIs). This is more meaningful than binary change detection (BCD) since it enables detailed change analysis in the observed areas. Previous works established triple-branch convolutional neural network (CNN) architecture...Show More
Existing deep neural network (DNN)-based image fusion methods seldom consider low-rank priors for the decomposition of source images, which cannot efficiently model base and detail components in images. To exploit the low-rank priors better, we propose a deep rank-N decomposition network (DRDec-Net) according to the rank-N decomposition of source images. Specifically, a rank-N decomposition model ...Show More
Spectral–Spatial Dual Graph Unfolding Network for Multispectral and Hyperspectral Image Fusion
Kai Zhang;Jun Yan;Feng Zhang;Chiru Ge;Wenbo Wan;Jiande Sun;Huaxiang Zhang
Recently, deep neural network (DNN)-based methods have achieved good results in terms of the fusion of low-spatial-resolution hyperspectral (LR HS) and high-spatial-resolution multispectral (HR MS) images. However, the spectral band correlation (SBC) and the spatial nonlocal similarity (SNS) in hyperspectral (HS) images are not sufficiently exploited by them. To model the two priors efficiently, w...Show More
Multiview Hypergraph Fusion Network for Change Detection in High-Resolution Remote Sensing Images
Xue Zhao;Kai Zhang;Feng Zhang;Jiande Sun;Wenbo Wan;Huaxiang Zhang
Currently, convolutional neural networks and transformers have been the dominant paradigms for change detection (CD) thanks to their powerful local and global feature extraction capabilities. However, with the improvement of resolution, spatial, spectral, and temporal relationships among objects in remote sensing images are becoming more complicated and cannot be modeled efficiently by the existin...Show More
Content-Guided Spatial–Spectral Integration Network for Change Detection in HR Remote Sensing Images
Yunlong Liu;Feng Zhang;Shanxin Zhang;Kai Zhang;Jiande Sun;Lorenzo Bruzzone
The integration of spatial and spectral information is beneficial to the improvement of change detection (CD) performance. However, existing methods cannot efficiently suppress the influences of spatial and spectral differences (SDs) in unchanged areas. To address these issues, in this article, we propose a content-guided spatial–spectral integration network (CSI-Net) for the fusion of global spat...Show More
DRFormer: Learning Disentangled Representation for Pan-Sharpening via Mutual Information- Based Transformer
In this article, we propose a new pan-sharpening method that disentangles low spatial resolution multispectral (LRMS) and panchromatic (PAN) images in terms of sensor-specific features and common features. These features are obtained by defining mutual information (MI)-based transformers designed to achieve disentangled learning. In the proposed method, LRMS and PAN images are cross-reconstructed ...Show More
Value-Added Evaluation Strategies in C Language Teaching
2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)
Year: 2023 | Conference Paper |
Based on the reflection of the research status, this study designs a value-added evaluation model that conforms to the characteristics of “C Language Programming”, and completes the teaching research and practice accordingly. Through the continuous vertical comparison of students' individual progress, the value-added level of students' knowledge and skills is reasonably quantified. Through the hor...Show More
Unsupervised Single-Generator CycleGAN-Based Pansharpening With Spatial-Spectral Degradation Modeling
Supervised pansharpening methods require the ground truth, which is generally unavailable. Therefore, the popularity of unsupervised pansharpening methods has increased. Generative adversarial networks (GANs) are often employed for unsupervised pansharpening, although achieving precise control over the generation process to capture rich spatial and spectral details is challenging. CycleGAN introdu...Show More
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution
Jiezhang Cao;Qin Wang;Yongqin Xian;Yawei Li;Bingbing Ni;Zhiming Pi;Kai Zhang;Yulun Zhang;Radu Timofte;Luc Van Gool
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (19)
Learning continuous image representations is recently gaining popularity for image super-resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary scales from low-resolution inputs. Existing methods mostly ensemble nearby features to predict the new pixel at any queried coordinate in the SR image. Such a local ensemble suffers from some limitations: i) it has no l...Show More
Event-Based Frame Interpolation with Ad-hoc Deblurring
Lei Sun;Christos Sakaridis;Jingyun Liang;Peng Sun;Kai Zhang;Jiezhang Cao;Qi Jiang;Kaiwei Wang;Luc Van Gool
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (15)
The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene. Even though previous works recognize the utility of asynchronous event information for this task, they ignore the fact that motion may or may not result in blur in the input video to be interpolated, depending on the length of the exposure time of the frames and the speed of ...Show More
LSDIR: A Large Scale Dataset for Image Restoration
Yawei Li;Kai Zhang;Jingyun Liang;Jiezhang Cao;Ce Liu;Rui Gong;Yulun Zhang;Hao Tang;Yun Liu;Denis Demandolx;Rakesh Ranjan;Radu Timofte;Luc Van Gool
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (37)
The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks. The datasets used to train these networks ‘only’ contain some thousands of images, which is still incomparable with the large scale datasets for other vision tasks such as visual recognition and object detection. The small...Show More
Denoising Diffusion Models for Plug-and-Play Image Restoration
Yuanzhi Zhu;Kai Zhang;Jingyun Liang;Jiezhang Cao;Bihan Wen;Radu Timofte;Luc Van Gool
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (58)
Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior. However, most existing methods focus on discriminative Gaussian denoisers. Although diffusion models have shown impressive performance for high-quality image synthesis, their potential to ser...Show More
NTIRE 2023 Challenge on Image Super-Resolution (×4): Methods and Results
Yulun Zhang;Kai Zhang;Zheng Chen;Yawei Li;Radu Timofte;Junpei Zhang;Kexin Zhang;Rui Peng;Yanbiao Ma;Licheng Jia;Huaibo Huang;Xiaoqiang Zhou;Yuang Ai;Ran He;Yajun Qiu;Qiang Zhu;Pengfei Li;Qianhui Li;Shuyuan Zhu;Dafeng Zhang;Jia Li;Fan Wang;Chunmiao Li;TaeHyung Kim;Jungkeong Kil;Eon Kim;Yeonseung Yu;Beomyeol Lee;Subin Lee;Seokjae Lim;Somi Chae;Heungjun Choi;ZhiKai Huang;YiChung Chen;YuanChun Chiang;HaoHsiang Yang;WeiTing Chen;HuaEn Chang;I-Hsiang Chen;ChiaHsuan Hsieh;SyYen Kuo;Ui-Jin Choi;Marcos V. Conde;Sunder Ali Khowaja;Jiseok Yoon;Ik Hyun Lee;Garas Gendy;Nabil Sabor;Jingchao Hou;Guanghui He;Zhao Zhang;Baiang Li;Huan Zheng;Suiyi Zhao;Yangcheng Gao;Yanyan Wei;Jiahuan Ren;Jiayu Wei;Yanfeng Li;Jia Sun;Zhanyi Cheng;Zhiyuan Li;Xu Yao;Xinyi Wang;Danxu Li;Xuan Cui;Jun Cao;Cheng Li;Jianbin Zheng;Anjali Sarvaiya;Kalpesh Prajapati;Ratnadeep Patra;Pragnesh Barik;Chaitanya Rathod;Kishor Upla;Kiran Raja;Raghavendra Ramachandra;Christoph Busch
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (17)
This paper reviews the NTIRE 2023 challenge on image super-resolution (×4), focusing on the proposed solutions and results. The task of image super-resolution (SR) is to generate a high-resolution (HR) output from a corresponding low-resolution (LR) input by leveraging prior information from paired LR-HR images. The aim of the challenge is to obtain a network design/solution capable to produce hig...Show More
NTIRE 2023 Challenge on Image Super-Resolution (×4): Methods and Results
Yulun Zhang;Kai Zhang;Zheng Chen;Yawei Li;Radu Timofte;Junpei Zhang;Kexin Zhang;Rui Peng;Yanbiao Ma;Licheng Jia;Huaibo Huang;Xiaoqiang Zhou;Yuang Ai;Ran He;Yajun Qiu;Qiang Zhu;Pengfei Li;Qianhui Li;Shuyuan Zhu;Dafeng Zhang;Jia Li;Fan Wang;Chunmiao Li;TaeHyung Kim;Jungkeong Kil;Eon Kim;Yeonseung Yu;Beomyeol Lee;Subin Lee;Seokjae Lim;Somi Chae;Heungjun Choi;ZhiKai Huang;YiChung Chen;YuanChun Chiang;HaoHsiang Yang;WeiTing Chen;HuaEn Chang;I-Hsiang Chen;ChiaHsuan Hsieh;SyYen Kuo;Ui-Jin Choi;Marcos V. Conde;Sunder Ali Khowaja;Jiseok Yoon;Ik Hyun Lee;Garas Gendy;Nabil Sabor;Jingchao Hou;Guanghui He;Zhao Zhang;Baiang Li;Huan Zheng;Suiyi Zhao;Yangcheng Gao;Yanyan Wei;Jiahuan Ren;Jiayu Wei;Yanfeng Li;Jia Sun;Zhanyi Cheng;Zhiyuan Li;Xu Yao;Xinyi Wang;Danxu Li;Xuan Cui;Jun Cao;Cheng Li;Jianbin Zheng;Anjali Sarvaiya;Kalpesh Prajapati;Ratnadeep Patra;Pragnesh Barik;Chaitanya Rathod;Kishor Upla;Kiran Raja;Raghavendra Ramachandra;Christoph Busch
Year: 2023 | Conference Paper |
NTIRE 2023 Video Colorization Challenge
Xiaoyang Kang;Xianhui Lin;Kai Zhang;Zheng Hui;Wangmeng Xiang;Jun-Yan He;Xiaoming Li;Peiran Ren;Xuansong Xie;Radu Timofte;Yixin Yang;Jinshan Pan;Zhong Zheng;Peng Qiyan;Zhang Jiangxin;Dong Jinhui;Tang Jinjing;Li Chichen;Lin Qipei Li;Qirong Liang;Ruipeng Gang;Xiaofeng Liu;Shuang Feng;Shuai Liu;Hao Wang;Chaoyu Feng;Furui Bai;Yuqian Zhang;Guangqi Shao;Xiaotao Wang;Lei Lei;Siqi Chen;Yu Zhang;Hanning Xu;Zheyuan Liu;Zhao Zhang;Yan Luo;Zhichao Zuo
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (22)
This paper reviews the video colorization challenge on the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2023. The target of this challenge is converting grayscale videos into color videos with better colorization performance and temporal consistency. The challenge consists of two tracks. For Track 1, the goal is achieving the best FID (Fréchet Inc...Show More
NTIRE 2023 Video Colorization Challenge
Xiaoyang Kang;Xianhui Lin;Kai Zhang;Zheng Hui;Wangmeng Xiang;Jun-Yan He;Xiaoming Li;Peiran Ren;Xuansong Xie;Radu Timofte;Yixin Yang;Jinshan Pan;Zhong Zheng;Peng Qiyan;Zhang Jiangxin;Dong Jinhui;Tang Jinjing;Li Chichen;Lin Qipei Li;Qirong Liang;Ruipeng Gang;Xiaofeng Liu;Shuang Feng;Shuai Liu;Hao Wang;Chaoyu Feng;Furui Bai;Yuqian Zhang;Guangqi Shao;Xiaotao Wang;Lei Lei;Siqi Chen;Yu Zhang;Hanning Xu;Zheyuan Liu;Zhao Zhang;Yan Luo;Zhichao Zuo
Year: 2023 | Conference Paper |
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
Xiaohong Liu;Xiongkuo Min;Wei Sun;Yulun Zhang;Kai Zhang;Radu Timofte;Guangtao Zhai;Yixuan Gao;Yuqin Cao;Tengchuan Kou;Yunlong Dong;Ziheng Jia;Yilin Li;Kai Zhao;Heng Cong;Hang Shi;Zhiliang Ma;Mirko Agarla;Zhiwei Huang;Hongye Liu;Ironhead Chuang;Haotian Fan;Shiqi Zhou;Yu Lai;Wenqi Wang;Haoning Wu;Chunzheng Zhu;Shiling Zhao;Hanene Brachemi Meftah;Tengfei Shi;Azadeh Mansouri
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (17)
This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual...Show More
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
Xiaohong Liu;Xiongkuo Min;Wei Sun;Yulun Zhang;Kai Zhang;Radu Timofte;Guangtao Zhai;Yixuan Gao;Yuqin Cao;Tengchuan Kou;Yunlong Dong;Ziheng Jia;Yilin Li;Kai Zhao;Heng Cong;Hang Shi;Zhiliang Ma;Mirko Agarla;Zhiwei Huang;Hongye Liu;Ironhead Chuang;Haotian Fan;Shiqi Zhou;Yu Lai;Wenqi Wang;Haoning Wu;Chunzheng Zhu;Shiling Zhao;Hanene Brachemi Meftah;Tengfei Shi;Azadeh Mansouri
Year: 2023 | Conference Paper |
The YOLOv5 network architecture prioritizes speed and efficiency, but this may limit its ability to capture intricate details of complex objects. To solve the problems of insufficient feature extraction ability and incomplete feature fusion in the YOLOv5 single-stage detection network, we propose a YOLOv5 algorithm based on an improved bidirectional feature pyramid network (BiFPN). The FPN is modi...Show More
Learning Deep Multiscale Local Dissimilarity Prior for Pansharpening
Kai Zhang;Guishuo Yang;Feng Zhang;Wenbo Wan;Man Zhou;Jiande Sun;Huaxiang Zhang
Various deep neural networks (DNNs) have been constructed to inject the spatial information of the panchromatic (PAN) image into the low spatial resolution multispectral (LR MS) image. However, most of them ignore the local dissimilarity (LD) prior between MS and PAN images, which has a negative influence on the fused image. Considering the above-mentioned issues, we propose a deep multiscale LD n...Show More
S-Feature Pyramid Network and Attention Module For Small Object Detection
Chuntao Wang;Pengcheng Dong;Jiande Sun;Zhenyong Lu;Kai Zhang;Wenbo Wan
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Because of the low resolution and limited information of small objects, and the computing resources are limited in practical applications, small object detection is still challenging. In order to improve the accuracy of small object detection, we propose a new method. It’s included a shallow feature pyramid network with an information extraction block at the shallow features and fused multi-scale ...Show More
A Unified Two-Stage Spatial and Spectral Network With Few-Shot Learning for Pansharpening
Zhi Sheng;Feng Zhang;Jiande Sun;Yanyan Tan;Kai Zhang;Lorenzo Bruzzone
Recently, pansharpening methods based on deep learning (DL) have achieved state-of-the-art results. However, current existing DL-based pansharpening methods need to be trained repetitively for different satellite sensors to obtain satisfactory fusion performance and therefore require a large number of training images for each satellite. To deal with these issues, in this article, we propose a unif...Show More
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