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CR-Famba: A Frequency-Domain Assisted Mamba for Thin Cloud Removal in Optical Remote Sensing imagery

IEEE Transactions on Multimedia 2025年

作者： Liu, Jiao Pan, Bin Shi, Zhenwei Nankai University TKLNDST College of Computer Science Tianjin300350 China Nankai University School of Statistics and Data Science KLMDASR LEBPS LPMC Tianjin300071 China Beihang University Image Processing Center School of Astronautics the State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China

Optical remote sensing images are inevitably affected by cloud cover. To remove clouds from optical remote sensing images, a series of deep learning-based thin cloud removal methods have been developed. However, these methods have not explored the long-range modeling ability of state space models in optical remote sensing image thin cloud removal. In this paper, we propose a frequency-domain assisted Mamba for thin cloud removal, which is called CR-Famba. In CR-Famba, to better extract global and local features of images, we design a frequency-domain assisted state space layer (FDA-SSL). The FDA-SSL consists of two core components: residual state space block (RSSB) and frequency domain detail enhancement block (FDDEB). The RSSB utilizes the visual state space module (VSSM) to extract long-range dependencies of images from a spatial perspective while adding convolutional layers to overcome local pixel forgetting. Due to the rich detailed information of remote sensing images, we present FDDEB equipped with discrete wavelet transform (DWT) to supplement the extracted local information from the frequency domain perspective. We conduct experiments on different types of cloud-containing datasets, and the results show that our method can recover images with clearer texture details compared to other methods. https://***/Lab-PANbin/ © 2025 IEEE.

关键词： Discrete wavelet transforms

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A Novel Mode Selection-Based Fast Intra Prediction Algorithm for Spatial SHVC 48

A Novel Mode Selection-Based Fast Intra Prediction Algorithm...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Dayong Sun, Yu Li, Weisheng Xie, Lele Lu, Xin Dufaux, Frederic Zhu, Ce Chongqing Key Laboratory on Big Data for Bio Intelligence Department of Computer Science China University of Central Arkansas Computing Department of Computer Science United States Chongqing Key Laboratory of Image Cognition School of Information and Communication Engineering China Chongqing University of Posts and Telecommunications School of Information and Communication Engineering China United Kingdom Université Paris-Saclay Cnrs CentraleSupélec Laboratoire des Signaux et Systèmes France University of Electronic Science and Technology of China School of Information and Communication Engineering China

ISBN: (纸本)9781728163277

Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss. © 2023 IEEE.

关键词： Gaussian distribution

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LCIQA: A Lightweight Contrastive-Learning Framework for image Quality Assessment via Cross-Scale Consistency Minimization

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Feng, Chenxi Min, Xiongkuo Ye, Long Yang, Yinghao Communication University of China State Key Laboratory of Media Convergence and Communication Beijing100024 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China Ministry of Education and School of Data Science and Media Intelligence Beijing100024 China

Blind image quality assessment (BIQA), which functions without the need for a reference image, is a challenging yet essential task in various image processing systems and downstream vision applications, ranging from semantic recognition to image enhancement. Traditionally, numerous BIQA models have been developed using supervised learning methodologies, which rely heavily on the availability and quality of ground truth data. To improve the generalization capability and robustness of these models, recent studies have explored the application of contrastive learning, aiming to enhance the quality representation capacity of model backbones through a self-supervised approach. However, the training process for contrastive learning is computationally intensive, posing significant challenges in resource-constrained environments. To mitigate this issue, we propose a Lightweight Contrastive-learning-based IQA (LCIQA) framework, designed to be efficiently trained on a single GPU without relying on ground truth data. This framework maintains a fixed vision backbone and focuses on optimizing the parameters of subsequent IQA heads through contrastive learning. To accommodate a lightweight framework, we incorporate a quality task adapter to eliminate semantic biases introduced by the features extracted from the fixed-parameter backbone. A coarse-to-fine contrastive learning strategy is then employed to train the quality regression module. Extensive experiments demonstrate the superior performance of our model in terms of both accuracy and complexity. In addition, ablation studies validate the effectiveness of each component within the proposed framework. © 1991-2012 IEEE.

关键词： Self-supervised learning

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Caiqing Jian Yongbin Qin Lihui Wang Chen Ye Xinyu Cheng GState Key Laboratory of Public Big Data College of Computer Science and Technology Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma.

关键词： Instance segmentation Measurement Visualization Adhesives Shape Semantic segmentation Glands Signal processing algorithms Signal processing Market research

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PP-LDG: A Medical Privacy-Preserving Labeled data Generation Framework

PP-LDG: A Medical Privacy-Preserving Labeled Data Generation...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Haoxiang Yuan Xiaochen Yuan Xiuli Bi Weisheng Li Guoyin Wang Bin Xiao Chongqing Key Laboratory of Image Cognition School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Faculty of Applied Sciences Macao Polytechnic University Macao China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

The rapid development of deep learning has led to an increasing demand for data. However, such data are scarce in many fields and often contain private and sensitive information, such as medical images. The fact that data owners are unwilling to share these high-privacy data further exacerbates the scarcity of data. A prevalent research direction is to combine differential privacy and image generation, by which we can obtain a large amount of synthetic data with a similar distribution to the original dataset, and that synthetic dataset does not compromise the privacy of the original dataset. However, these methods cannot provide labels for the generated data, limiting the subsequent uses of the data. To solve the problem, we propose a generic privacy-preserving labeled data generation framework named PP-LDG. It is the first data publishing framework to generate labeled data while not disclosing the privacy of original data. We theoretically demonstrate that our proposed framework can provide strict privacy guarantees with differential privacy and demonstrate the effectiveness of the synthetic dataset obtained from the framework under practical privacy budgets through extensive experiments.

关键词： Deep learning Differential privacy Accuracy Limiting image synthesis Publishing data collection Synthetic data Biomedical imaging image classification

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Multi-Scale Spatial Transform Network for Atmospheric Polarization Prediction 11th

Multi-Scale Spatial Transform Network for Atmospheric Polari...

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11th International Conference on image and Graphics, ICIG 2021

作者： Dang, Tianyi Liu, Yating Gao, Xinjian Xie, Linfang Yan, Yuan Chen, Qian Gao, Jun School of Computer and Information Hefei University of Technology Hefei China Image Information Processing Laboratory Hefei University of Technology Hefei China School of Data Science University of Science and Technology of China Hefei China

ISBN: (纸本)9783030873547

Sequential atmospheric polarization patterns can provide information for navigation when cues from a satellite source are not available. However, the real scenarios with extreme environments often cause corruption of captured atmospheric polarization data which makes the navigation unreliable. So this encourages us to focus on the atmospheric polarization patterns prediction (APP) topic. In this paper, we try to investigate and fill the gap in this task. We first propose a dataset called the Temporal Polarization 1072 (TP1072) dataset to compensate for the lack of the dataset, which makes future research more possible. And further, we propose a novel and efficient deep learning sequential prediction model named Multi-Scale Spatial Transform Network (MSST-Net) for that topic. The overall model includes a Spatial Transform decoder (STD) and an adversarial learning-based Physical Property Learning (PPL) strategy. The STD makes the model can perceive the sequential pattern easily which significantly increases prediction accuracy. And the PPL constrains the overall model to achieve more accurate physical property. The extensive experiments prove that both two modules are complement with each other and also demonstrate that our model can effectively capture the spatio-temporal cues of the atmospheric polarization mode. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Research and application of artificial intelligence based webshell detection model: A literature review

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Webshell, as the"culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing nature of webshells pose significant challenges to the corresponding detection schemes. With the rise of Artificial Intelligence (AI) technology, researchers have started to apply different intelligent algorithms and neural network architectures to the task of webshell detection. However, the related research still lacks a systematic and standardized methodological process, which is confusing and redundant. Therefore, following the development timeline, we carefully summarize the progress of relevant research in this field, dividing it into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. We further elaborate on the main characteristics and core algorithms of each stage. In addition, we analyze the pain points and challenges that still exist in this field and predict the future development trend of this field from our point of view. To the best of our knowledge, this is the first review that details the research related to AI-based webshell detection. It is also hoped that this paper can provide detailed technical information for more researchers interested in AI-based webshell detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Research and application of Transformer based anomaly detection model: A literature review

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Transformer, as one of the most advanced neural network models in Natural Language Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR image Reconstruction

arXiv

引用

arXiv 2024年

作者： Qiao, Xiaoyu Li, Weisheng Wang, Guofen Huang, Yuping Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China College of Computer and Information Science Chongqing Normal University Chongqing China

Deep unfolding networks (DUNs) have demonstrated significant potential in accelerating magnetic resonance imaging (MRI). However, they often encounter high computational costs and slow convergence rates. Besides, they struggle to fully exploit the complementarity when incorporating multiple priors. In this study, we propose a multi-prior collaborative DUN, termed MCU-Net, to address these limitations. Our method features a parallel structure consisting of different optimization-inspired subnetworks based on low-rank and sparsity, respectively. We design a gates-controlled spatial attention module (GSAM), evaluating the relative confidence (RC) and overall confidence (OC) maps for intermediate reconstructions produced by different subnetworks. RC allocates greater weights to the image regions where each subnetwork excels, enabling precise element-wise collaboration. We design correction modules to enhance the effectiveness in regions where both subnetworks exhibit limited performance, as indicated by low OC values, thereby obviating the need for additional branches. The gate units within GSAMs are designed to preserve necessary information across multiple iterations, improving the accuracy of the learned confidence maps and enhancing robustness against accumulated errors. Experimental results on multiple datasets show significant improvements on PSNR and SSIM results with relatively low FLOPs compared to cutting-edge methods. Additionally, the proposed strategy can be conveniently applied to various DUN structures to enhance their performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A review of big data technology and its application in cancer care

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Computers in Biology and Medicine 2024年 176卷 108577-108577页

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Hua, Dianbo Liu, Fengchun Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future. © 2024 Elsevier Ltd

关键词： Big data

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