Signal Processing最新文献_第5页

Quaternion-based deep image prior with regularization by denoising for color image restoration

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-17 DOI: 10.1016/j.sigpro.2024.109883

Qinghua Zhang , Liangtian He , Shaobing Gao , Liang-Jian Deng , Jun Liu

{"title":"Quaternion-based deep image prior with regularization by denoising for color image restoration","authors":"Qinghua Zhang , Liangtian He , Shaobing Gao , Liang-Jian Deng , Jun Liu","doi":"10.1016/j.sigpro.2024.109883","DOIUrl":"10.1016/j.sigpro.2024.109883","url":null,"abstract":"<div><div>Deep image prior (DIP) has demonstrated remarkable efficacy in addressing various imaging inverse problems by capitalizing on the inherent biases of deep convolutional architectures to implicitly regularize the solutions. However, its application to color images has been hampered by the conventional DIP method’s treatment of color channels in isolation, ignoring their important inter-channel correlations. To mitigate this limitation, we extend the DIP framework from the real domain to the quaternion domain, introducing a novel quaternion-based deep image prior (QDIP) model specifically tailored for color image restoration. Moreover, to enhance the recovery performance of QDIP and alleviate its susceptibility to the unfavorable overfitting issue, we propose incorporating the concept of regularization by denoising (RED). This approach leverages existing denoisers to regularize inverse problems and integrates the RED scheme into our QDIP model. Extensive experiments on color image denoising, deblurring, and super-resolution demonstrate that the proposed QDIP and QDIP-RED algorithms perform competitively with many state-of-the-art alternatives, both in quantitative and qualitative assessments. The code and data are available at the website: <span><span>https://github.com/qiuxuanzhizi/QDIP-RED</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109883"},"PeriodicalIF":3.4,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Robust beampattern control for steerable frequency-invariant beamforming in the presence of sensor imperfections

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-13 DOI: 10.1016/j.sigpro.2025.109893

Congwei Feng, Huawei Chen

{"title":"Robust beampattern control for steerable frequency-invariant beamforming in the presence of sensor imperfections","authors":"Congwei Feng, Huawei Chen","doi":"10.1016/j.sigpro.2025.109893","DOIUrl":"10.1016/j.sigpro.2025.109893","url":null,"abstract":"<div><div>Wideband beamformers are known sensitive to sensor imperfections, especially for small-sized sensor arrays. Mean performance optimization (MPO) is a commonly used criterion for the design of robust wideband beamformers in the presence of sensor imperfections, which aims to synthesize the mean beampattern. However, the existing designs for robust wideband beamformers cannot guarantee precise control of the mean beampattern. In this paper, we propose a MPO-criterion-based robust design approach for steerable wideband beamformers (SWBs) using a weighted spatial response variation (SRV) measure. By exploiting the increased degrees of freedom provided by the weighted-SRV, the proposed robust SWB design can achieve a frequency-invariant mean beampattern with both mainlobe inconsistency and sidelobe level being able to be precisely controlled. We develop a theory and the corresponding algorithm to find the solution for the weighting function of the weighted-SRV-based cost function to achieve precise mean beampattern control. Some insights into the effect of sensor imperfections on the achievable frequency invariance are also revealed. The effectiveness of the proposed design is verified by the simulation results.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109893"},"PeriodicalIF":3.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

TDU-DLNet: A transformer-based deep unfolding network for dictionary learning

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-13 DOI: 10.1016/j.sigpro.2025.109886

Kai Wu , Jing Dong , Guifu Hu , Chang Liu , Wenwu Wang

{"title":"TDU-DLNet: A transformer-based deep unfolding network for dictionary learning","authors":"Kai Wu , Jing Dong , Guifu Hu , Chang Liu , Wenwu Wang","doi":"10.1016/j.sigpro.2025.109886","DOIUrl":"10.1016/j.sigpro.2025.109886","url":null,"abstract":"<div><div>Deep unfolding attempts to leverage the interpretability of traditional model-based algorithms and the learning ability of deep neural networks by unrolling model-based algorithms as neural networks. Following the framework of deep unfolding, some conventional dictionary learning algorithms have been expanded as networks. However, existing deep unfolding networks for dictionary learning are developed based on formulations with pre-defined priors, e.g., <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-norm, or learn priors using convolutional neural networks with limited receptive fields. To address these issues, we propose a transformer-based deep unfolding network for dictionary learning (TDU-DLNet). The network is developed by unrolling a general formulation of dictionary learning with an implicit prior of representation coefficients. The prior is learned by a transformer-based network where an inter-stage feature fusion module is introduced to decrease information loss among stages. The effectiveness and superiority of the proposed method are validated on image denoising. Experiments based on widely used datasets demonstrate that the proposed method achieves competitive results with fewer parameters as compared with deep learning and other deep unfolding methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109886"},"PeriodicalIF":3.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 Two-Stream Temporal-Frequency transformer network for underwater acoustic target recognition

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-13 DOI: 10.1016/j.sigpro.2025.109891

Dongyao Bi , Lijun Zhang , Jie Chen

引用次数: 0

The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-13 DOI: 10.1016/j.sigpro.2025.109894

Jasin Machkour , Michael Muma , Daniel P. Palomar

{"title":"The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control","authors":"Jasin Machkour , Michael Muma , Daniel P. Palomar","doi":"10.1016/j.sigpro.2025.109894","DOIUrl":"10.1016/j.sigpro.2025.109894","url":null,"abstract":"<div><div>We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the original predictors and multiple sets of randomly generated dummy predictors. A finite sample proof based on martingale theory for the FDR control property is provided. Numerical simulations confirm that the FDR is controlled at the target level while allowing for high power. We prove that the dummies can be sampled from any univariate probability distribution with finite expectation and variance. The computational complexity of the proposed method is linear in the number of variables. The T-Rex selector outperforms state-of-the-art methods for FDR control in numerical experiments and on a simulated genome-wide association study (GWAS), while its sequential computation time is more than two orders of magnitude lower than that of the strongest benchmark methods. The open source R package TRexSelector containing the implementation of the T-Rex selector is available on CRAN.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109894"},"PeriodicalIF":3.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ASRMF: Adaptive image super-resolution based on dynamic-parameter DNN with multi-feature prior

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-12 DOI: 10.1016/j.sigpro.2024.109880

Zhe Zhang, Ke Wang, Lan Cheng, Xinying Xu

引用次数: 0

A comprehensive survey on stegomalware detection in digital media, research challenges and future directions

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-10 DOI: 10.1016/j.sigpro.2025.109888

Laila Tul Badar, Barbara Carminati, Elena Ferrari

{"title":"A comprehensive survey on stegomalware detection in digital media, research challenges and future directions","authors":"Laila Tul Badar, Barbara Carminati, Elena Ferrari","doi":"10.1016/j.sigpro.2025.109888","DOIUrl":"10.1016/j.sigpro.2025.109888","url":null,"abstract":"<div><div>Stegomalware is a malicious activity that employs steganography techniques to hide malicious code within innocent-looking files. The hidden code can then be executed to launch attacks on the victim’s computer or network. Unlike traditional malware, which performs malicious activities by executing its code, stegomalware is specifically designed to evade detection by hiding its malicious payload within seemingly harmless media files, making it difficult to detect using traditional anti-virus and anti-malware tools. To counter stegomalware, numerous steganalysis techniques have been developed for different digital media, such as images, audio, video, text, and networks. This survey presents a comprehensive and detailed overview of stegomalware, covering its background, techniques, modes of attacks, and evasion techniques in various digital media applications. It also provides notable case studies of stegomalware attacks and in-depth review of recent steganalysis approaches. In addition, the survey reviews widely used stegomalware tools and datasets. Finally, it discusses the limitations of state-of-the-art approaches and outlines related research trends.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109888"},"PeriodicalIF":3.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LSCANet: Differential features guided long–short cross attention network for infrared and visible image fusion

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-09 DOI: 10.1016/j.sigpro.2025.109889

Baofeng Guo , Hongtao Huo , Xiaowen Liu , Bowen Zheng , Jing Li

{"title":"LSCANet: Differential features guided long–short cross attention network for infrared and visible image fusion","authors":"Baofeng Guo , Hongtao Huo , Xiaowen Liu , Bowen Zheng , Jing Li","doi":"10.1016/j.sigpro.2025.109889","DOIUrl":"10.1016/j.sigpro.2025.109889","url":null,"abstract":"<div><div>Infrared and visible image fusion can generate images that not only highlight prominent targets, but also contain rich details and texture information. However, directly fusing the features of infrared and visible images can diminish the correlation information between source images. To address this, we propose a differential features guided long–short cross attention network for infrared and visible image fusion (LSCANet). Specifically, a differential feature cross attention network (DCAN) is designed to achieve cross modal multi-scale interaction of differential features in the feature extraction process. Cross modal feature interaction before infrared and visible features fusion can enhance deep feature relationships between cross modal features, thereby preserving more correlation information between source images. Besides, a long–short differential feature attention network (LSDAN) is proposed to achieve the integration of multi-scale cross-modal differential features, which can preserve details and texture information while reducing the artifacts and noise introduced during the integration process. Moreover, the loss function is introduced to impel the network retain more details and texture information while preserving thermal radiation information. Ablation experiments were conducted to validate the effectiveness of LSCANet. Extensive qualitative and quantitative experiments conducted on cross dataset benchmarks have demonstrated that LSCANet outperforms eight state-of-the-art methods. The source code is available at <span><span>https://github.com/Pinot-30/LSCANet/tree/main</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"231 ","pages":"Article 109889"},"PeriodicalIF":3.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

High-order chirplet transform for efficient reconstruction of multicomponent signals with amplitude modulated crossing ridges

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-08 DOI: 10.1016/j.sigpro.2025.109887

Timothée Maison , Fabrice Silva , Nathalie Henrich Bernardoni , Philippe Guillemain

引用次数: 0

QP-adaptive compressed video super-resolution with coding priors

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-06 DOI: 10.1016/j.sigpro.2024.109878

Tingrong Zhang, Zhengxin Chen, Xiaohai He, Chao Ren, Qizhi Teng

{"title":"QP-adaptive compressed video super-resolution with coding priors","authors":"Tingrong Zhang, Zhengxin Chen, Xiaohai He, Chao Ren, Qizhi Teng","doi":"10.1016/j.sigpro.2024.109878","DOIUrl":"10.1016/j.sigpro.2024.109878","url":null,"abstract":"<div><div>Video super-resolution algorithms have found widespread applications as post-processing techniques in down-sampling based coding methods. With the advancements in deep learning techniques, video super-resolution has achieved remarkable success. However, applying existing video super-resolution methods to compressed videos requires training specific models for various quantization parameters (QPs), significantly increasing the resource consumption for model training and compromising their practical utility. To address this issue, we propose a QP-adaptive network for compressed video super-resolution based on coding priors (QPAN). Firstly, we design a QP modulation module (QPMM), which can utilize the frame-wise QP to recalibrate feature maps. Then, on the basis of QPMM, an adaptive multi-scale prior fusion module (Ada-MSPFM) and an adaptive enhancement modulation module (Ada-EMM) are constructed. The former effectively integrates multi-scale features from spatial coding priors in the bitstream and multi-scale features from the decoded video frames. And the latter improves the expressive ability of the network by leveraging QP modulation and reinforcing feature flow adaptively. Extensive experiments demonstrate the highly flexible and adaptive of our proposed method, which exhibits superior reconstruction performance compared to state-of-the-art video super-resolution algorithms.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"230 ","pages":"Article 109878"},"PeriodicalIF":3.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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