Digital Signal Processing最新文献_第8页

Prediction model for newly-added sensors to ocean buoys: Leveraging adversarial loss and deep residual LSTM architecture 海洋浮标新增传感器的预测模型：利用对抗损失和深度残差 LSTM 架构

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-09 DOI: 10.1016/j.dsp.2025.105126

Qiguang Zhu , Zhen Shen , Wenjing Qiao , Zhen Wu , Hongbo Zhang , Ying Chen

{"title":"Prediction model for newly-added sensors to ocean buoys: Leveraging adversarial loss and deep residual LSTM architecture","authors":"Qiguang Zhu , Zhen Shen , Wenjing Qiao , Zhen Wu , Hongbo Zhang , Ying Chen","doi":"10.1016/j.dsp.2025.105126","DOIUrl":"10.1016/j.dsp.2025.105126","url":null,"abstract":"<div><div>Adding new sensors to ocean buoys can extend their measurement range. However, due to the lack of historical data, the prediction model for the newly-added sensors suffers from the difficulty of training. Traditional pre-training methods simply use the same type of labelled data from other sea areas as pre-training data, failing to take into account the distributional differences between the features of data from different regions, so that the pre-training effect still has a large space to rise. Aiming at the above problems, this paper proposes a prediction model for newly-added sensors to ocean buoys based on adversarial loss and depth residual Long Short Term Memory. Firstly, this paper constructs a prediction model based on deep residual Long Short Term Memory. Then, the pre-training effect is improved by introducing adversarial loss in the loss function of the pre-training task. Finally, the model performance is validated on buoy monitoring data in the nearshore waters of Beihai City, Guangxi Province. The results showed that the pre-training effect was significantly improved with the introduction of adversarial loss compared to the traditional pre-training method.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105126"},"PeriodicalIF":2.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Branch-YOLO: An efficient object detector for thin structure objects like pantograph

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-09 DOI: 10.1016/j.dsp.2025.105121

Yaqian Li , Jiaqi Han , Haibin Li , Wenming Zhang

{"title":"Branch-YOLO: An efficient object detector for thin structure objects like pantograph","authors":"Yaqian Li , Jiaqi Han , Haibin Li , Wenming Zhang","doi":"10.1016/j.dsp.2025.105121","DOIUrl":"10.1016/j.dsp.2025.105121","url":null,"abstract":"<div><div>The pantograph is a critical component of the railway Pantograph-OCS system, making it essential to accurately detect its lifting and lowering states to ensure efficient and safe operation. However, there are two principal problems that hampers progress in accurate real-time detection. The pantograph's unique slender structure occupies only a few pixels, making effective feature extraction difficult and detection accuracy susceptible to interference from complex outdoor application scenarios. In this work, we aim to propose a generalized detection model for branch-like objects with thin structures and multi-scale sizes, using train pantograph states detection as a case study. To this end, we propose the Branch-YOLO based on YOLOv8, which is improved in two aspects, feature extraction and feature fusion. Firstly, we introduce the BrLayer which consists of BranchConv and EFAC (Extend Receptive Field Attention Convolution). The BranchConv can adaptively capture features of thin and tortuous local structures, while the EFAC contributes to expanding the effective receptive field. Subsequently, we propose a feature fusion network (BranchNet) to integrate multi-level semantic information and multi-scale features, significantly reducing background interference in detecting slender structure objects and enhancing the ability to perceive variable object scales. Besides, we propose the LckLayer (Lightweight Cross-Kernel Convolution layer) and introduce the FdM (Feature Decomposition module) in BranchNet for lightweight design, reducing the computational overhead and enhancing model efficiency. Branch-YOLO we proposed not only achieves the best performance on the multi-scale pantograph datasets but also attains an outstanding 42.9% AP on the COCO val2017 datasets, with 5.9M parameters and 16.8 GFLOPs.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"162 ","pages":"Article 105121"},"PeriodicalIF":2.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved YOLACT algorithm for instance segmentation of stacking parts

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-08 DOI: 10.1016/j.dsp.2025.105145

Yongsheng Chao, Xiaochen Zhang, Guolin Rong

引用次数: 0

Variable fractional order-based structure-texture aware Retinex model with dynamic guidance illumination

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-08 DOI: 10.1016/j.dsp.2025.105140

Chengxue Li, Chuanjiang He

{"title":"Variable fractional order-based structure-texture aware Retinex model with dynamic guidance illumination","authors":"Chengxue Li, Chuanjiang He","doi":"10.1016/j.dsp.2025.105140","DOIUrl":"10.1016/j.dsp.2025.105140","url":null,"abstract":"<div><div>Image Retinex is developed for decomposition of an observed image into the illumination and reflectance components. In this paper, we introduce a general framework of variational model with dynamic guidance illumination for image Retinex, consisting of two coupled minimization problems. The first minimization problem is responsible for estimation of the illumination and reflectance components from the input image, and the other is used to dynamically update the guidance illumination under the control of the illumination prior. As a particular case of the proposed framework, we present an adaptive variable fractional order-based structure-texture aware Retinex model with dynamic guidance illumination. In the proposed model, the illumination prior is derived from the local maximum of the maximal RGB value in the input color image, followed by guided filtering. Qualitative and quantitative evaluations on three commonly-used datasets illustrate that the proposed model generally achieves higher performance in image decomposition with application to low-light enhancement, in comparison to several state-of-the-art Retinex-based models. In particular, ARISM and LOE metrics of the proposed model ranks in the top two across the three datasets.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"161 ","pages":"Article 105140"},"PeriodicalIF":2.9,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new texture descriptor based on hexagonal local binary pattern for content-based image retrieval

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-07 DOI: 10.1016/j.dsp.2025.105138

Sadegh Fadaei , Mehdi Azadimotlagh , Armin Rashno , Amin Beheshti

{"title":"A new texture descriptor based on hexagonal local binary pattern for content-based image retrieval","authors":"Sadegh Fadaei , Mehdi Azadimotlagh , Armin Rashno , Amin Beheshti","doi":"10.1016/j.dsp.2025.105138","DOIUrl":"10.1016/j.dsp.2025.105138","url":null,"abstract":"<div><div>Texture features play a vital role in content-based image retrieval (CBIR) applications. Most texture extraction methods have a low accuracy and high feature vector length. This paper presents a novel hexagonal local binary pattern (HLBP) to extract more informative and compact features from images. To have robust patterns against rotation, rotation invariant hexagonal patterns are presented using cyclic set theory. Texture feature vector is extracted from hexagonal images based on proposed patterns and used in CBIR application. To evaluate proposed method, experiments are performed in five datasets Corel-1k, Brodatz, VisTex, Corel-10k, and STex. The proposed HLBP method outperforms square local binary pattern (SLBP) in images with noise in the terms of precision. The feature vector length of the proposed method is 64, which is much shorter than those in competitive methods and leads to high speed in retrieval phase. The best performance of the proposed method is revealed in texture datasets which achieved the highest precision among all competitive methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"161 ","pages":"Article 105138"},"PeriodicalIF":2.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Local-global aggregation transformer for enhanced image super-resolution

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-07 DOI: 10.1016/j.dsp.2025.105127

Yuxiang Wu , Xiaoyan Wang , Yuzhao Gao , Xiaoyan Liu , Yan Dou

{"title":"Local-global aggregation transformer for enhanced image super-resolution","authors":"Yuxiang Wu , Xiaoyan Wang , Yuzhao Gao , Xiaoyan Liu , Yan Dou","doi":"10.1016/j.dsp.2025.105127","DOIUrl":"10.1016/j.dsp.2025.105127","url":null,"abstract":"<div><div>Recent advancements in Transformer-based methods have significantly improved image super-resolution (SR) tasks, outperforming traditional CNN-based approaches. However, most existing Transformer-based methods focus predominantly on global dependencies while neglecting local information, thereby limiting their effectiveness. To address this, we propose the Local-Global Aggregation Transformer (LGAT), which combines convolution-based attention and window-based self-attention to leverage both global statistics and strong local fitting capabilities. Additionally, we introduce the Spatial Frequency Fusion Block (SFFB) to model long-range dependencies and enhance feature expression. Furthermore, we propose a novel Spatial-Gate Multi-Layer Perception (SGMLP) to mine additional non-linear spatial information and reduce redundancy. Extensive experiments on benchmark datasets demonstrate that LGAT achieves impressive performance, outperforming state-of-the-art SR methods both objectively and subjectively. Our contributions include the development of LGAT, which utilizes both local and global features for better reconstruction, the introduction of SGMLP and SFFB, and the demonstration of LGAT's effectiveness through extensive experiments.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"161 ","pages":"Article 105127"},"PeriodicalIF":2.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SEP analysis on the κ-μ shadowed fading with additive Laplacian noise

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-07 DOI: 10.1016/j.dsp.2025.105135

Puspraj Singh Chauhan , Paresh Chandra Sau , Sandeep Kumar , Ankit Jain , Vimal Bhatia

引用次数: 0

A multi-scale refinement corner detection algorithm based on Shi-Harris

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-07 DOI: 10.1016/j.dsp.2025.105137

Man Deng , Fan Yang , QingRong Chen , Jian Wang , Si Sun , Bo Qi

{"title":"A multi-scale refinement corner detection algorithm based on Shi-Harris","authors":"Man Deng , Fan Yang , QingRong Chen , Jian Wang , Si Sun , Bo Qi","doi":"10.1016/j.dsp.2025.105137","DOIUrl":"10.1016/j.dsp.2025.105137","url":null,"abstract":"<div><div>Corner detectors provide more structural and localization information with less redundancy than interest point detectors, which are very important in image processing tasks. This paper proposes a novel multi-scale refinement corner detection algorithm based on the Harris response function improved by Shi-Tomasi, integrating scale information into every step of corner extraction in the extremum point space. First, a new image information criterion, the spectral product, is proposed by analyzing the relationship between amplitude and frequency after Fourier transform of an image. This criterion combines the number of intensity variations represented by the frequency with the response strength represented by the amplitude. It is used to compute the mathematical relationship between scale and feature quantity, thereby constructing a corner density space. The number of stable corners at each scale is adaptively determined. Secondly, a new multi-scale separable gradient kernel is designed to adapt to scale variations and precisely compute image gradients. The proposed method further refines the sizes of the corner detection window and the non-maximal suppression window using hierarchical scale information. Finally, the criteria on region repeatability evaluation based on the Vggaffine dataset (under affine image transformation, JPEG compression, and viewpoint transformations) and Hpatches dataset (under viewpoint transformations) are used to evaluate the proposed detector against ten state-of-the-art methods. Experimental results demonstrate that the proposed detector outperforms all the tested methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"161 ","pages":"Article 105137"},"PeriodicalIF":2.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interrupted sampling repeater jamming suppression based on Gaussian mixture model and sparse reconstruction

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-06 DOI: 10.1016/j.dsp.2025.105132

Zhenhua Liu , Wei Liang , Ning Fu , Liyan Qiao , Jun Zhang

{"title":"Interrupted sampling repeater jamming suppression based on Gaussian mixture model and sparse reconstruction","authors":"Zhenhua Liu , Wei Liang , Ning Fu , Liyan Qiao , Jun Zhang","doi":"10.1016/j.dsp.2025.105132","DOIUrl":"10.1016/j.dsp.2025.105132","url":null,"abstract":"<div><div>The interrupted sampling repeater jamming (ISRJ) based on digital radio frequency memory (DRFM) is coherent with the radar transmitted signal, allowing it to obtain partial pulse compression gain while exhibiting both suppression and deception jamming effects, posing a significant threat to modern radars. In this article, we propose an ISRJ suppression method based on Gaussian Mixture Model (GMM) and sparse reconstruction. Firstly, the probability distribution of the amplitudes of the received echoes is fitted using a GMM to obtain the mean value of each component. Based on the magnitude and the discontinuous nature of ISRJ in the time domain, jamming-free signal segments are extracted. Subsequently, these segments are processed through de-chirping, and by leveraging their sparse characteristics in the frequency domain, the complete target echo is obtained through sparse reconstruction. Matching filtering is then applied to the reconstructed target echo, followed by peak searching, to detect the genuine target echo, thereby achieving ISRJ suppression. Experimental results demonstrate that this method exhibits high accuracy in extracting the jamming-free signal segments and excellent ISRJ suppression performance, while also demonstrating strong robustness against different jamming parameters.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"161 ","pages":"Article 105132"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep clustering framework for underwater image recognition

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2025-03-06 DOI: 10.1016/j.dsp.2025.105131

Lei Zhao , Xiao-Lei Zhang , Kunde Yang

{"title":"A deep clustering framework for underwater image recognition","authors":"Lei Zhao , Xiao-Lei Zhang , Kunde Yang","doi":"10.1016/j.dsp.2025.105131","DOIUrl":"10.1016/j.dsp.2025.105131","url":null,"abstract":"<div><div>Underwater image recognition plays a crucial role in assessing the health status of marine ecosystems. By utilizing underwater cameras and image recognition technology, researchers can monitor the biodiversity, population numbers, growth status, and overall structure and functionality of ecosystems in the ocean. However, the problem of marine ecology assessment always occurs in dynamic and open environments, and discoveries of unknown new species are often made. Existing works which applied classification methods directly may not address this situation well. Therefore, unsupervised learning is needed to cluster these newly emerged species. However, due to strong noise interference in underwater images, clustering the unlabeled samples directly is difficult. To address this issue, we propose a two-stage training framework that can learn discriminative knowledge from labeled data for clustering new classes. Its core idea is to utilize pseudo-labeling to train the model, and then strengthens the capability of clustering by leveraging the consistency between the labeled and unlabeled data. Furthermore, contrastive learning is also used to optimize the model's representation in the embedding space. Experimental results on the WildFish dataset of over 5000 species verified the effectiveness of the proposed method in open-set underwater image recognition.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"161 ","pages":"Article 105131"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0