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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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DSSNet: a transformer-based network for dense scene text detection and recognition in complex environments 5

DSSNet: a transformer-based network for dense scene text det...

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5th International Conference on Signal Processing and computer science, SPCS 2024

作者： Li, Jing Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This paper presents a novel approach for dense scene text detection called DSSNet (Dense Script Spotter Network). The network leverages ResNet and FPN for feature extraction, employing multi-scale feature fusion and Transformer-based feature processing to enhance text recognition across varying sizes. The method generates text instance shapes using Bézier central curves and performs text recognition by integrating positional query information. Experimental results on the DSTD1500 and ICDAR2015 datasets demonstrate that DSSNet outperforms existing methods in terms of text localization accuracy, recognition accuracy, and annotation flexibility. © 2025 SPIE.

关键词： Feature extraction

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Deep optics framework utilizing a liquid crystal microlens array for dynamic wavefront sensing and correction in atmospheric turbulence

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Optics Letters 2025年第11期50卷 3584-3587页

作者： Song, Xiaoyue Li, Hui Li, Zikang Li, Shiqi Wu, Yuntao School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Intelligent Robot Wuhan430205 China

Anisotropic atmospheric turbulence, which distorts wavefronts, poses challenges to advancements in fields such as astronomy and remote sensing. Existing reconstruction methods often fail to adequately capture its spatiotemporal variability, leading to suboptimal performance. To address this, we propose a spatiotemporal quantization-aware deep optics framework. This framework integrates light field physics models with a data-driven neural network via a liquid crystal microlens array (LC-MLA), offering a solution to dynamic turbulence issues. The framework constructs a spatiotemporal transfer function that integrates wavefront sensing, high-dimensional spatiotemporal quantization-aware turbulence modeling, and neural network characterization. By synergizing dynamic light field data with electronically tunable LC-MLA, our approach optimizes hardware and software parameters for efficient, precise correction of multi-field, multi-order aberrations. In experiments, this approach optimizes real-time turbulence correction and offers a 17.5% improvement in peak signal-to-noise ratio compared to previous methods. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Wavefronts

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Infrared and Visible Image Fusion Based on Region Semantic Discrimination Optimization 5

Infrared and Visible Image Fusion Based on Region Semantic D...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Li, Juan Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798350385557

The fusion of infrared and visible images holds significant application value in military, security, environmental monitoring, and other fields. However, infrared images exhibit distinct target features, while visible images contain clear texture details. The fused image should preserve the corresponding features of the infrared or visible source images based on different semantic regions. In this study, based on the semantic discrimination network module, we propose a generative adversarial network with dual discriminators to improve the fusion effect of infrared and visible images. This method extracts semantic information from images through a semantic segmentation network and combines no-reference image quality assessment and entropy to estimate the information content in each semantic object, thereby guiding the fusion process. Experimental results demonstrate that this method can effectively achieve the fusion of infrared and visible images, achieving significant improvements in feature preservation. © 2024 IEEE.

关键词： Image fusion

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GeoSAM: A Sampling and Attention Mechanism for Cross-View Geo-Localisation 6

GeoSAM: A Sampling and Attention Mechanism for Cross-View Ge...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Wu, Fan Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798331541750

Cross-view geo-localisation aims to match street-view images with satellite images, but this task remains challenging due to significant appearance and viewpoint discrepancies. This paper proposes a novel framework, GeoSAM, which enhances feature alignment and matching accuracy by integrating a dynamic sampling strategy and attention mechanism. GeoSAM employs a dual-branch network with Convolutional Block Attention Modules (CBAM) to strengthen feature extraction for both street-view and satellite-view images. Furthermore, the framework incorporates a dynamic hard negative sampling strategy, combining geographic proximity and visual similarity, to ensure robustness in contrastive learning. Experiments on the CVUSA dataset demonstrate that GeoSAM significantly outperforms existing methods in terms of Top-1 accuracy and recall, offering an efficient and generalisable solution for cross-view geo-localisation tasks. © 2024 IEEE.

关键词： Image matching

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D-KAN Model: Enhancing Power Load Forecasting with Kolmogorov-Arnold Networks 4

D-KAN Model: Enhancing Power Load Forecasting with Kolmogoro...

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4th International Conference on Energy engineering and Power Systems, EEPS 2024

作者： Jiang, Mengyuan Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798350366914

As the demand for power load forecasting continues to grow in modern society, this paper proposes an improved D-KAN model that integrates Kolmogorov-Arnold Networks (KAN) into the DLinear power load time series prediction framework. The model utilizes KAN's learnable activation functions, parameterized by spline functions, to enhance the accuracy of electricity demand forecasting. This study focuses on the electricity load data from Huanggang City, hubei province, covering the period from April 2022 to July 2023. Experimental results show that the D-KAN model performs more accurately in short-term predictions compared to the DLinear model. © 2024 IEEE.

关键词： D-KAN Deep Learning electricity load forecasting Kolmogorov-Arnold Networks (KAN) Machine Learning styling Time Series Analysis

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ID-DA Fusion:Infrared and Visible Image Fusion Based on Intermediate Domain and Dual Attention Mechanism 6

ID-DA Fusion:Infrared and Visible Image Fusion Based on Inte...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Liu, Shuo Zhou, Huabing Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot College of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798331541750

Infrared and visible image fusion aims to integrate complementary information from source images to produce a fused image with enhanced visual quality and richer semantic content. In this study, we propose a novel fusion framework based on the Intermediate Domain and a Dual Attention Mechanism (ID-DA Fusion). The intermediate domain is introduced to effectively bridge the gap between infrared and visible modalities, enabling better feature alignment and information preservation. Additionally, the dual attention mechanism dynamically adjusts the feature weights by focusing on salient regions and interdependencies between channels, enhancing both global and local feature representation. Experimental results on benchmark datasets demonstrate that the proposed method achieves superior performance in terms of edge preservation, contrast enhancement, and detail retention, outperforming state-of-the-art fusion techniques in quantitative metrics and visual assessments. This approach holds great potential for applications in surveillance, remote sensing, and medical imaging. © 2024 IEEE.

关键词： Image fusion

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BEDiff: denoising diffusion probabilistic models for building extraction

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Optoelectronics Letters 2025年第5期21卷 298-305页

作者： LEI Yanjing WANG Yuan CHAN Sixian HU Jie ZHOU Xiaolong ZHANG Hongkai College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information China Three Gorges University Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University College of Electrical and Information Engineering Quzhou University School of Electronic and Information Engineering Anhui Jianzhu University

Accurately identifying building distribution from remote sensing images with complex background information is challenging. The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds. Building on this concept, we propose a novel framework, building extraction diffusion model(BEDiff), which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion. Our approach begins with the design of booster guidance, a mechanism that extracts structural and semantic features from remote sensing images to serve as priors, thereby providing targeted guidance for the diffusion process. Additionally, we introduce a cross-feature fusion module(CFM) that bridges the semantic gap between different types of features, facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively. Our proposed BEDiff marks the first application of diffusion models to the task of building extraction. Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff, affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.

关键词： A

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Super-resolution reconstruction of remote sensing images based on multi-scale reparameterized pyramids 8

Super-resolution reconstruction of remote sensing images bas...

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8th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2025

作者： Huang, RuiNan Zheng, GengSheng Wuhan Institute of Technology Hubei Province Key Laboratory of Intelligent Robot Wuhan China Wuhan Institute of Technology School of Computer Science & Engineering Wuhan China

ISBN: (纸本)9798331517915

In order to solve the problems of loss of structural details and insufficient attention to edge information in the existing lightweight remote sensing super-resolution reconstruction methods, a multi-scale reparameterized pyramid network (MRPN) method for remote sensing image super-resolution reconstruction based on multi-scale reparameterized pyramids was proposed. The model is based on the deep residual network structure, and multiple cascading multi-scale reparameterized pyramid modules are designed in the network for feature extraction. In the module, a multi-scale separation convolution and fusion module is designed to help the network refine image information, and the unglobal and global feature expressions are enhanced, and the link-enhanced spatial attention module is used to improve the attention to high-frequency feature information. Finally, the jumping connection method is used to fuse multi-level features to achieve effective spatial information fusion. The model is verified by 2x, 3x and 4x super-resolution reconstruction on the NWPU-RESISC45 dataset and the AID dataset, and the experimental results show that the peak signal-to-noise ratio and structural similarity are improved, and the method has good performance in improving image quality and preserving details. © 2025 IEEE.

关键词： Image enhancement

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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