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9th International Conference on signal and image processing (ICSIP)

作者： Xue, Wenli Zhu, Hongwei Nian, Zhongyuan Wu, Xueyang Cui, Mingshi Mu, Chunfang Yang, Weiming Chen, Zhigang State Grid East Inner Mongolia Informat & Telecom Hohhot Peoples R China

ISBN: (纸本)9798350350920

Traditional channel estimation often relies on methods such as compressive sensing for computation. However, the selection of parameters significantly impacts estimation accuracy. To address this issue, we propose a deep learning-based channel estimation framework. Employing a fully connected network to process the results of traditional channel estimation enhances the accuracy of channel estimation. Additionally, considering the temporal characteristics of the data, we construct an LSTM network for data processing. The final output is obtained by weighted summation of multiple processed results. The use of deep learning algorithms effectively addresses the accuracy limitations present in traditional channel estimation. Simulation results indicate that the proposed channel estimation algorithm in this paper exhibits a high level of accuracy.

关键词： channel estimation compressed sensing least squares deep learning

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Learned Reconstruction methods With Convergence Guarantees: A survey of concepts and applications

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IEEE signal processing MAGAZINE 2023年第1期40卷 164-182页

作者： Mukherjee, Subhadip Hauptmann, Andreas Oktem, Ozan Pereyra, Marcelo Schonlieb, Carola-Bibiane Univ Bath Dept Comp Sci Machine Learning & Artificial Intelligence Bath BA2 7PB England Univ Oulu Computat Math Res Unit Math Sci Oulu 90014 Finland UCL Dept Comp Sci London WC1E 6BT England Uppsala Univ Dept Informat Technol Computat Sci S-75237 Uppsala Sweden KTH Royal Inst Technol Dept Math Numer Anal S-11428 Stockholm Sweden Heriot Watt Univ Maxwell Inst Math Sci Edinburgh EH14 4AS Scotland Heriot Watt Univ Sch Math & Comp Sci Edinburgh EH14 4AS Scotland Univ Cambridge Appl Math Cambridge CB3 0WA England Cambridge Image Anal Grp Cambridge England EPSRC Cambridge Math Informat Healthcare Hub Cambridge England

In recent years, deep learning has achieved remarkable empirical success for image reconstruction. This has catalyzed an ongoing quest for the precise characterization of the correctness and reliability of data-driven methods in critical use cases, for instance, in medical imaging. Notwithstanding the excellent performance and efficacy of deep learning-based methods, concerns have been raised regarding the approaches' stability, or lack thereof, with serious practical implications. Significant advances have been made in recent years to unravel the inner workings of data-driven image recovery methods, challenging their widely perceived black-box nature. In this article, we specify relevant notions of convergence for data-driven image reconstruction, which forms the basis of a survey of learned methods with mathematically rigorous reconstruction guarantees. An example that is highlighted is the role of input-convex neural networks (ICNNs), offering the possibility to combine the power of deep learning with classical convex regularization theory for devising methods that are provably convergent. This survey article is aimed at both methodological researchers seeking to advance the frontiers of our understanding of data-driven image reconstruction methods as well as practitioners by providing an accessible description of useful convergence concepts and by placing some of the existing empirical practices on a solid mathematical foundation.

关键词： Deep learning Learning systems neural networks Closed box Reconstruction algorithms image reconstruction Reliability

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Adapting Few-Shot Classification via In-Process Defense

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IEEE TRANSACTIONS ON image processing 2024年 33卷 5232-5245页

作者： Yang, Xi Kong, Dechen Lin, Ren Wang, Nannan Gao, Xinbo Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Elect Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China

Most few-shot learning methods employ either adaptive approaches or parameter amortization techniques. However, their reliance on pre-trained models presents a significant vulnerability. When an attacker's trigger activates a hidden backdoor, it may result in the misclassification of images, profoundly affecting the model's performance. In our research, we explore adaptive defenses against backdoor attacks for few-shot learning. We introduce a specialized stochastic process tailored to task characteristics that safeguards the classification model against attack-induced incorrect feature extraction. This process functions during forward propagation and is thus termed an "in-process defense." Our method employs an adaptive strategy, effectively generating task-level representations, enabling rapid adaptation to pre-trained models, and proving effective in few-shot classification scenarios for countering backdoor attacks. We apply latent stochastic processes to approximate task distributions and derive task-level representations from the support set. This task-level representation guides feature extraction, leading to backdoor trigger mismatching and forming the foundation of our parameter defense strategy. Benchmark tests on Meta-Dataset reveal that our approach not only withstands backdoor attacks but also shows an improved adaptation in addressing few-shot classification tasks.

关键词： Adaptation models Feature extraction Training Predictive models Metalearning Few shot learning Context modeling Visual classification backdoor attack few-shot feature adaptation neural processes

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Enhanced Swin Transformer and Edge Spatial Attention for Remote Sensing image Semantic Segmentation

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IEEE signal processing LETTERS 2025年 32卷 1296-1300页

作者： Liu, Fuxiang Hu, Zhiqiang Li, Lei Li, Hanlu Liu, Xinxin Beijing Inst Technol Minist Educ Key Lab Dynam & Control Flight Vehicle Beijing 100081 Peoples R China Chongqing Vocat Inst Tourism Chongqing 409099 Peoples R China North Nav Control Technol Co Ltd Beijing 100176 Peoples R China

Combining convolutional neural networks (CNNs) and transformers is a crucial direction in remote sensing image semantic segmentation. However, due to differences in the spatial information focus and feature extraction methods, existing feature transfer and fusion strategies do not effectively integrate the advantages of both approaches. To address these issues, we propose a CNN-transformer hybrid network for precise remote sensing image semantic segmentation. We propose a novel Swin Transformer block to optimize feature extraction and enable the model to handle remote sensing images of arbitrary sizes. Additionally, we design an Edge Spatial Attention module to focus attention on local edge structures, effectively integrating global features and local details. This facilitates efficient information flow between the Transformer encoder and CNN decoder. Finally, a multi-scale convolutional decoder is employed to fully leverage both global information from the Transformer and local features from the CNN, leading to accurate segmentation results. Our network achieved state-of-the-art performance on the Vaihingen and Potsdam datasets, reaching mIoU and F1 scores of 67.37% and 79.82%, as well as 72.39% and 83.68%, respectively.

关键词： Transformers image edge detection Feature extraction Convolution Vegetation Semantic segmentation Decoding Remote sensing Data mining Training Edge detection remote sensing image semantic segmentation Swin transformer

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Advancing Hyperspectral and Multispectral image Fusion: An Information-Aware Transformer-Based Unfolding Network

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IEEE TRANSACTIONS ON neural NETWORKS AND LEARNING SYSTEMS 2025年第4期36卷 7407-7421页

作者： Sun, Jianqiao Chen, Bo Lu, Ruiying Cheng, Ziheng Qu, Chunhui Yuan, Xin Xidian Univ Natl KeyLaboratory Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Sch Cyber Engn Xian 710126 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Zhejiang Peoples R China

In hyperspectral image (HSI) processing, the fusion of the high-resolution multispectral image (HR-MSI) and the low-resolution HSI (LR-HSI) on the same scene, known as MSI-HSI fusion, is a crucial step in obtaining the desired high-resolution HSI (HR-HSI). With the powerful representation ability, convolutional neural network (CNN)-based deep unfolding methods have demonstrated promising performances. However, limited receptive fields of CNN often lead to inaccurate long-range spatial features, and inherent input and output images for each stage in unfolding networks restrict the feature transmission, thus limiting the overall performance. To this end, we propose a novel and efficient information-aware transformer-based unfolding network (ITU-Net) to model the long-range dependencies and transfer more information across the stages. Specifically, we employ a customized transformer block to learn representations from both the spatial and frequency domains as well as avoid the quadratic complexity with respect to the input length. For spatial feature extractions, we develop an information transfer guided linearized attention (ITLA), which transmits high-throughput information between adjacent stages and extracts contextual features along the spatial dimension in linear complexity. Moreover, we introduce frequency domain learning in the feedforward network (FFN) to capture token variations of the image and narrow the frequency gap. Via integrating our proposed transformer blocks with the unfolding framework, our ITU-Net achieves state-of-the-art (SOTA) performance on both synthetic and real hyperspectral datasets.

关键词： Feature extraction Transformers Task analysis image reconstruction Frequency-domain analysis Hyperspectral imaging Spatial resolution Attention mechanism hyperspectral imaging image fusion vision transformers (ViTs)

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CT image Precise Denoising Model with Edge Based Segmentation with Labeled Pixel Extraction Using CNN Based Feature Extraction for Oral Cancer Detection

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TRAITEMENT DU signal 2023年第3期40卷 1297-1304页

作者： Krisha, Patibandla Sandhya Peram, Subba Rao Vignans Fdn Sci Technol & Res Univ Sch Comp & Informat Dept IT & Comp Applicat Guntur 522213 Andhra Pradesh India Vignans Nirula Inst Technol & Sci Women Dept IT Peda Palakaluru 522009 Andhra Pradesh India

Oral cancer, the most prevalent form of head and neck cancer, calls for early detection to ensure better patient outcomes, reducing morbidity and mortality rates. This study explores the application of computer vision and deep learning methods for photographic images in the oral cancer domain, investigating a two-stage pipeline for an automated system to identify oral potentially malignant abnormalities. Oral cancer staging, crucial for determining appropriate treatment and medication, often faces challenges due to noise levels in images that impact disease prediction accuracy. This research works with an image dataset, enhancing image quality and performing denoising to improve accuracy levels. The study aims to evaluate the accuracy of an image enhancement and denoising model, resulting in quality images for extracting features for oral cancer detection. By segmenting the image using multiscale morphology methods, cell features can be extracted. The morphological edge detection method enables more precise extraction of target, cell area, perimeter, and other multi-dimensional features, followed by classification through Convolution neural Networks (CNN). This research proposes a Precise Denoising Model with Edge-Based Segmentation for Labeled Pixel Extraction with Fixed Feature Set (PDM-ES-LPE-FFS) for relevant feature extraction. When compared with traditional models, the proposed model demonstrates superior performance.

关键词： oral cancer image processing image processing segmentation denoising edge detection pixel extraction image quality enhancement feature extraction

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Hyperspectral image synthesis from sparse RGB data: a comparative study combining linear regression, multilayer perceptron, and clustering

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signal image AND VIDEO processing 2024年第2期18卷 1625-1633页

作者： Magalhaes, Antonio Hamilton Yehia, Hani Camille Magalhaes, Hermes Aguiar Pontificia Univ Catolica Minas Gerais Dept Elect & Telecommun Belo Horizonte MG Brazil Univ Fed Minas Gerais Grad Program Elect Engn Belo Horizonte MG Brazil

The problem of synthesizing hyperspectral images from RGB images is ill posed, with potentially infinite solutions, as it involves estimating data in a high-dimensional space, associated with hyperspectral bands, from limited information in a three-dimensional RGB space. However, under certain conditions related to lighting and physical properties of natural scenes, a feasible solution can be found. This study evaluates four methods for estimating hyperspectral data from RGB images: ridge linear regression, Minibatch K-means followed by linear regression, a multilayer perceptron (MLP) neural network, and Minibatch K-means combined with an MLP neural network. The results of each method are compared with each other and with the NTIRE 2020 Challenge. The comparison was performed using the mean absolute relative error (MARE) and execution time. The MLP method attained the lowest MARE (0.072) but with the longest execution time (220 s). Ridge regression attained the shortest execution time (0.47 s) at the cost of a higher MARE (0.089). The best trade-off was obtained by combining Minibatch K-means clustering with MLP, which reduced the execution time by 16 times (13.8 s) with a slightly higher MARE (0.075) compared to MLP alone. We have also confirmed that, for the case of natural scenes, points representing pixels that are close to each other in the RGB space are also close to each other in the hyperspectral space.

关键词： Hyperspectral image Machine Learning neural Networks RGB spectral recovery

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An reconstruction bidirectional recurrent neural network -based deinterleaving method for known radar signals in open-set scenarios

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IET RADAR SONAR AND NAVIGATION 2024年第6期18卷 965-981页

作者： Zheng, Haiping Xie, Kai Zhu, Yingshen Lin, Jinjian Wang, Lihong Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Peoples R China Beijing Inst Radio Measurement Beijing Peoples R China

In electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open-set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent neural Network (RBi-RNN). RBi-RNN utilises input reconstruction and employs a joint training strategy incorporating cross-entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter-class latent representation distances while minimising intra-class disparities. By incorporating an open-set recognition method based on extreme value theory, RBi-RNN adapts to open-set scenarios. Simulation results demonstrate the superiority of RBi-RNN over conventional models in both closed-set and open-set scenarios. In open-set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses. In electronic warfare, radar signal deinterleaving is crucial. Existing deinterleaving deep-learning models trained with known radar classes struggle in open-set scenarios. The authors introduce reconstruction bidirectional recurrent neural network (RBi-RNN), a novel model incorporating input reconstruction and a joint training strategy. RBi-RNN can distinguish known and unknown radar signals within interleaved pulse sequences and deinterleave the known radar classes. image

关键词： electronic countermeasures electronic warfare radar radar signal processing signal classification

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EMNet: Efficient Multi-Scale Convolutional Network for image Restoration 17

EMNet: Efficient Multi-Scale Convolutional Network for Image...

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17th International Congress on image and signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Lian, Yaoze Zhou, Liang School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9798331507398

With the rise of deep learning, numerous methods based on convolutional neural networks have emerged in various fields of image restoration. neural networks and recent advancements like transformers have performed exceptionally well in many visual tasks. In this paper, we analyze the strengths and weaknesses of convolutional methods and transformer approaches. We integrate a Multi-scale Enhanced Convolution module with dilation mechanisms and a Multi-shape Feature Enhanced Unit with various attention combinations into the MixBlock, effectively achieving both a large receptive field and local information interaction. Additionally, our model leverages a U-shaped architecture, enabling it to effectively capture multiscale representations. We introduce the network as EMNet-a highly efficient CNN architecture specifically designed for image restoration while ensuring computational efficiency. Extensive experiments demonstrate that our network achieves state-of-the-art performance on three benchmark datasets, including image dehazing, desnowing, and motion deblurring. © 2024 IEEE.

关键词： Convolutional neural networks

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Adaptive Surveillance Video Compression With Background Hyperprior

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IEEE signal processing LETTERS 2025年 32卷 456-460页

作者： Zhao, Yu Tang, Song Ye, Mao Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China

neural surveillance video compression methods have demonstrated significant improvements over traditional video compression techniques. In current surveillance video compression frameworks, the first frame in a Group of Pictures (GOP) is usually compressed fully as an I frame, and the subsequent P frames are compressed by referencing this I frame at Low Delay P (LDP) encoding mode. However, this compression approach overlooks the utilization of background information, which limits its adaptability to different scenarios. In this paper, we propose a novel Adaptive Surveillance Video Compression framework based on background hyperprior, dubbed as ASVC. This background hyperprior is related with side information to assist in coding both the temporal and spatial domains. Our method mainly consists of two components. First, the background information from a GOP is extracted, modeled as hyperprior and is compressed by exiting methods. Then these hyperprior is used as side information to compress both I frames and P frames. ASVC effectively captures the temporal dependencies in the latent representations of surveillance videos by leveraging background hyperprior for auxiliary video encoding. The experimental results demonstrate that applying ASVC to traditional and learning based methods significantly improves performance.

关键词： Surveillance Video compression Encoding image reconstruction Redundancy image coding Video sequences Training Gaussian distribution Bit rate Surveillance compression learned surveillance coding background hyperprior video coding

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