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Kai Zhang - IEEE Xplore Author Profile

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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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