Jingjing Liu - IEEE Xplore Author Profile

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Optical remote sensing images (RSIs) has received widespread attention in fields such as agricultural monitoring, mineral exploration, and military defense. However, the detection performance will be seriously degraded when interfered by noise. To overcome this issue, we first present a novel method called tensor low-rank approximation (TLRA), which leverages the weighted tensor nuclear norm (WTNN...Show More
Advanced SPICE Model for high electron mobility transistor (ASM-HEMT) is one of the industry standard models for GaN-based high electron mobility transistors (HEMTs), in which there still lack analytical models for the distribution of the physical properties inside the device, especially a model for the distribution of the transverse electric field (EX) and carrier concentration (nS) that can be l...Show More
While attention-based approaches have shown considerable progress in enhancing image fusion and addressing the challenges posed by long-range feature dependencies, their efficacy in capturing local features is compromised by the lack of diverse receptive field extraction techniques. To overcome the shortcomings of existing fusion methods in extracting multi-scale local features and preserving glob...Show More
Sparse linear discriminant analysis (LDA) is a popular machine learning method that improves the accuracy of data classification by introducing sparsity. However, its performance often degrades seriously when encountering noise. To address this issue, this paper proposes a new method called efficient and robust sparse linear discriminant analysis (ERSLDA). The core idea is to characterize the loca...Show More
This paper proposes a bicubic interpolation method based on Clamp-Laplace composite filters, to solve the problems of blurring and distortion in interpolated images generated by traditional interpolation methods. In addition, the proposed method is implemented in hardware based on ZCU104 FPGA platform. The qualitative and quantitative experimental results show that the proposed method improves PSN...Show More
Image editing methods based on diffusion models are significantly superior to traditional methods. However, due to their slow sampling speed, high computational complexity, and weak data generalization ability, these have encountered certain limitations in practical applications. This paper proposes an efficient sparse blocks inference method for diffusion models to address this issue. It also com...Show More
Network pruning has gained popularity for reducing storage and computational requirements in deep neural network models. However, common pruning techniques focus on the single granularity without sufficiently exploring collaborative research on different granularity levels. In order to explore the combination of different pruning granularities while effectively compressing the network in a structu...Show More
This paper proposes a rectangular approximation method for curved-shape power density to rapidly calculate the temperature profile of the entire chip by using the 2D thermal model with the effective thermal characteristic length. The proposed method employs rectangular blocks to approximate the non-rectangular power density areas, thereby enabling the utilization of stepwise integration for the de...Show More
In the field of video display, with the increases of resolution, the problems of transmission bandwidth, display adaptation and image display quality have gradually become prominent. To solve the above problems, we propose a new adaptive filtering interpolation scaling algorithm composed of an adaptive Laplacian filter and a bilinear interpolation scaling algorithm. The Laplacian filter alleviates...Show More
This brief introduces a high-performance flipping-input synchronized switch harvesting on inductor and capacitors (FI-SSHIC) piezoelectric energy harvesting interface circuit. The unique design approach involves segmenting the flipping stages, enabling the FI-SSHIC to execute 17 phase flips utilizing merely a diminutive inductor of $5\mu \text{H}$ and 8 compact capacitors. By circumventing the t...Show More
This article proposes a multimode neuromorphic event-frame integrated vision sensor that enables event detection (ED) with simultaneous brightness measurement based on the pulse width modulation mechanism. The logarithmic voltage is directly taken as intensity information. Brightness measurement involves in-pixel voltage-to-pulse conversion and out-pixel pulse coding. The maximum event bandwidth i...Show More
Deep convolutional neural networks have significantly enhanced the performance of single image super-resolution in recent years. However, the majority of the proposed networks are single-channel, making it challenging to fully exploit the advantages of neural networks in feature extraction. This paper proposes a Multi-attention Fusion Recurrent Network (MFRN), which is a multiplexing architecture-...Show More
K-SVD, as a sparse representation method of the over-complete dictionary, has been widely used in image processing. However, the K-SVD dictionary requires to be bigger, resulting in a large amount of computation and longtime consumption during the experiment. Therefore, an efficient distributed K-SVD approach (DK-SVD+) for image reconstruction is proposed. Firstly, the image is decomposed into sma...Show More
In simultaneous localization and mapping (SLAM) and structure from motion (SFM), bundle adjustment (BA) plays a vital role in adjusting the pose of the 6-DOF camera and the location of the 3D landmark points. However, the usefulness of the BA is affected by the number of optimization variables. Thus, an efficient bundle adjustment (EBA) based on a fast iteration technique can effectively estimate ...Show More
This paper proposes a new infrared and visible image fusion method based on the densely connected disentangled representation generative adversarial network (DCDR-GAN), which strips the content and the modal features of infrared and visible images through disentangled representation (DR) and fuses them separately. To deal with the mutually exclusive features in infrared and visible images, inject ...Show More
Multifocus image fusion has attracted considerable attention because it can overcome the physical limitations of optical imaging equipment and fuse multiple images with different depths of the field into one full-clear image. However, most existing deep learning-based fusion methods concentrate on the segmentation of focus–defocus regions, resulting in the loss of the details near the boundaries. ...Show More
In order to improve the denoising performance of dynamic vision sensor, an efficient denoising method based on Markov Random Field (MRF) is proposed in this paper, which achieves better denoising effects by minimizing constraint energy functions. Firstly, an optimization technique for the fastest solution is presented by using the monotonicity of the energy function and duality of the DVS output. ...Show More
To break through the limitation of dynamic vision sensor (DVS) in object recognition and classification, a pulse width modulation (PWM) imaging method in a multi-mode DVS which integrates temporal contrast detection with brightness measurement is proposed. The improved logarithmic photoreceptor with pulse coding strategy ensures a theoretical intra-scene dynamic range of 130 dB. The 128×128 vision...Show More
In order to improve the denoising performance of dynamic vision sensor, an efficient space spatiotemporal noise filter (ESSNF) is proposed in this paper, which achieved excellent results in the performance index of normalized variance. Firstly, the manhattan distance and adaptive weight are utilized to describe the spatiotemporal correlation of events. Secondly, The experiment was evaluated by nor...Show More
With the rapid development of modern science and continuous innovation of technology, a large number of power quality data storage and transmission has caused great burden to power system operation, so it is of great practical significance to study power quality. In order to solve the problem of the existing fixed orthogonal sparse basis is not enough to represent the unknown power quality signals...Show More
Mask R-CNN is a recently proposed state-of-the-art algorithm for object instance segmentation. However, it is still a challenge to improve the accuracy of Mask R-CNN. In this paper, a novel Mask R-CNN approach is proposed. Firstly, we combine global context modeling with Mask R-CNN by capturing long-range dependencies. Secondly, we extend the Mask R-CNN approach by fusing a new simplified non-loca...Show More
Low-rank matrix restoration has recently become an essential research topic in video processing especially for moving object detection. RPCA is a classical model for lowrank matrix restoration, which can be utilized to represent the original image as the superposition of a low-rank matrix and a sparse matrix. While existing methods based on RPCA are usually limited by redundant background which hi...Show More
Low-rank and sparse decomposition based image alignment has recently become an important research topic in the computer vision community. However, the reconstruction process often suffers from the perturbations caused by variations of the input samples. The reason behind is that the consistency of the learned low-rank and sparse structures for similar input samples is not well addressed in the exi...Show More
Foreground extraction has been a fundamental research topic in video analysis. Recently the non-convex penalty has been shown remarkable advantages in signal processing, and the fused sparsity is widely employed in image processing because it is able to preserve sharp edges that are usually the most important for clear object extraction. By decomposing video frames into low-rank background and spa...Show More
Moving object detection from video data is one of the most active research points in computer vision area. Recently the $\ell$2,0-norm has shown remarkable advantages in signal processing, and the total variation (TV) has been widely used in image processing due to its ability to preserve sharp edges, which are usually the most important for clear object extraction. In this paper, firstly, a unifi...Show More