Signal Processing-Image Communication最新文献_第7页

Research on an adaptive robust ellipse fitting method integrating multiple weight strategies 多权策略集成的自适应鲁棒椭圆拟合方法研究

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-10-11 DOI: 10.1016/j.image.2025.117425

Bo-Lin Jian , Chao-Chung Peng , Wen-Lin Chu

{"title":"Research on an adaptive robust ellipse fitting method integrating multiple weight strategies","authors":"Bo-Lin Jian , Chao-Chung Peng , Wen-Lin Chu","doi":"10.1016/j.image.2025.117425","DOIUrl":"10.1016/j.image.2025.117425","url":null,"abstract":"<div><div>This study aims to develop a robust ellipse fitting method that maintains computational efficiency comparable to Direct Least Squares (DLS) while achieving superior noise resistance for image processing and shape recognition applications. We propose Adaptive Dual-Robust Ellipse Fitting (ADREF), which integrates three novel contributions: (1) adaptive candidate clustering using k-nearest neighbor distances to adjust epsilon values, eliminating fixed parameter dependency automatically; (2) dual-robust weighting strategy combining Huber and Tukey Biweight functions with mixing coefficient α = 0.5, enhanced by image grayscale intensity information; and (3) nonlinear minimization correction based on geometric distance for improved parameter accuracy. Experimental validation across noise levels (σ = 1 to 11) demonstrates ADREF's superior performance over traditional methods. Compared to DLS, Weighted Least Squares (WLS), and Iteratively Reweighted Least Squares (IRLS), ADREF achieves the lowest errors in center coordinates, axis lengths, and rotation angles. Under high-noise conditions (σ = 11), ADREF maintains stable performance while other methods deteriorate significantly. Multi-ellipse experiments confirm automatic identification and fitting capabilities without pre-specifying target numbers. ADREF provides a breakthrough solution combining computational efficiency with robust noise resistance, particularly suitable for high-noise industrial vision applications, medical image analysis, and pattern recognition systems requiring accuracy and reliability.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117425"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321926","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

DUALF-D: Disentangled dual-hyperprior approach for light field image compression 光场图像压缩的解纠缠双超先验方法

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-11-13 DOI: 10.1016/j.image.2025.117436

Soheib Takhtardeshir , Roger Olsson , Christine Guillemot , Mårten Sjöström

{"title":"DUALF-D: Disentangled dual-hyperprior approach for light field image compression","authors":"Soheib Takhtardeshir , Roger Olsson , Christine Guillemot , Mårten Sjöström","doi":"10.1016/j.image.2025.117436","DOIUrl":"10.1016/j.image.2025.117436","url":null,"abstract":"<div><div>Light field (LF) imaging captures spatial and angular information, offering a 4D scene representation enabling enhanced visual understanding. However, high dimensionality and redundancy across spatial and angular domains present major challenges for compression, particularly where storage, transmission bandwidth, or processing latency are constrained. We present a novel Variational Autoencoder (VAE)-based framework that explicitly disentangles spatial and angular features using two parallel latent branches. Each branch is coupled with an independent hyperprior model, allowing more precise distribution estimation for entropy coding and finer rate–distortion control. This dual-hyperprior structure enables the network to adaptively compress spatial and angular information based on their unique statistical characteristics, improving coding efficiency. To further enhance latent feature specialization and promote disentanglement, we introduce a mutual information-based regularization term that minimizes redundancy between the two branches while preserving feature diversity. Unlike prior methods relying on covariance-based penalties prone to collapse, our information-theoretic regularizer provides more stable and interpretable latent separation. Experimental results on publicly available LF datasets demonstrate our method achieves strong compression performance, yielding an average <strong>BD-PSNR gain of 2.91 dB over HEVC</strong> and high compression ratios (e.g., 200:1). Additionally, our design enables fast inference, with a total <strong>end-to-end time</strong> over <strong>19x faster</strong> than the JPEG Pleno standard, making it well-suited for real-time and bandwidth-sensitive applications. By jointly leveraging disentangled representation learning, dual-hyperprior modeling, and information-theoretic regularization, our approach offers a scalable, effective solution for practical light field image compression.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117436"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519980","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

YUW-Net: A Y-shaped deep network for enhancement of Under-water images YUW-Net：用于增强水下图像的y形深度网络

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-10-11 DOI: 10.1016/j.image.2025.117422

Rupankar Das, Chandrajit Choudhury

{"title":"YUW-Net: A Y-shaped deep network for enhancement of Under-water images","authors":"Rupankar Das, Chandrajit Choudhury","doi":"10.1016/j.image.2025.117422","DOIUrl":"10.1016/j.image.2025.117422","url":null,"abstract":"<div><div>One of the main challenges in underwater imaging is the poor quality caused by the underwater propagation of light. In this work, this distortion is analyzed separately along the color and illumination channels, and accordingly a novel Convolution neural network (CNN) architecture is proposed for the restoration of underwater images. The proposed method is independent of any approximated physical model or parameter-based model for underwater image formation. In the proposed approach the distortion is perceived as changes in the color and illumination distribution of the image, majorly in terms of the first and second moments. Extensive experimentation are done for evaluating the proposed network over real underwater images alone and in combination with the synthetically generated images. To prove the claims, a thorough comparison with the state-of-the-art works is presented, in qualitative as well as quantitative manner. The proposed network is found to outperform the state-of-the-art methods. For easy reproducibility of the work, on acceptance, the implementation of this work will be shared in <span><span>https://github.com/ChandrajitChoudhury/YUWNET</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117422"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321925","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

Video and text semantic center alignment for text-video cross-modal retrieval 文本-视频跨模态检索的视频和文本语义中心对齐

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-09-25 DOI: 10.1016/j.image.2025.117413

Ming Jin , Huaxiang Zhang , Lei Zhu , Jiande Sun , Li Liu

{"title":"Video and text semantic center alignment for text-video cross-modal retrieval","authors":"Ming Jin , Huaxiang Zhang , Lei Zhu , Jiande Sun , Li Liu","doi":"10.1016/j.image.2025.117413","DOIUrl":"10.1016/j.image.2025.117413","url":null,"abstract":"<div><div>With the proliferation of video on the Internet, users demand higher precision and efficiency of retrieval technology. The current cross-modal retrieval technology mainly has the following problems: firstly, there is no effective alignment of the same semantic objects between video and text. Secondly, the existing neural networks destroy the spatial features of the video when establishing the temporal features of the video. Finally, the extraction and processing of the text’s local features are too complex, which increases the network complexity. To address the existing problems, we proposed a text-video semantic center alignment network. First, a semantic center alignment module was constructed to promote the alignment of semantic features of the same object across different modalities. Second, a pre-trained BERT based on a residual structure was designed to protect spatial information when inferring temporal information. Finally, the “jieba” library was employed to extract the local key information of the text, thereby simplifying the complexity of local feature extraction. The effectiveness of the network structure was evaluated on the MSVD, MSR-VTT, and DiDeMo datasets.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117413"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222573","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

HDRUnet3D: High dynamic range image reconstruction network with residual and illumination maps HDRUnet3D：带有残差和光照贴图的高动态范围图像重建网络

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-10-30 DOI: 10.1016/j.image.2025.117428

Fengyuan Wu , Jiuzhe Wei , Qinghong Sheng , Xiang Liu , Bo Wang , Jun Li

{"title":"HDRUnet3D: High dynamic range image reconstruction network with residual and illumination maps","authors":"Fengyuan Wu , Jiuzhe Wei , Qinghong Sheng , Xiang Liu , Bo Wang , Jun Li","doi":"10.1016/j.image.2025.117428","DOIUrl":"10.1016/j.image.2025.117428","url":null,"abstract":"<div><div>In the process of reconstructing high dynamic range (HDR) images from multi-exposed low dynamic range (LDR) images, removing the ghosting artifacts induced by the movement of objects and the misalignment of dynamic scenes is a pivotal challenge. Previous methods attempt to mitigate ghosting artifacts using optical flow registration or motion pixel rejection. However, these approaches are prone to errors due to inaccuracies in motion estimation and the lack of effective information guidance during pixel exclusion. This article proposes a novel attention-guided end-to-end deep neural network named HDRUnet3d, which treats multi-exposed LDR images as video stream and utilizes 3D convolution to extract both temporal and spatial features, enabling the network to adaptively capture the temporal dynamics of moving objects. Moreover, two information-guided feature enhancement modules is proposed, consisting of the Residual Map-Guided Attention Module and the Illumination-guided Local Enhanced Module. The former utilizes gamma-corrected residual images to guide the learning of temporal motion semantics information, while the latter adaptively enhance local features based on illumination maps. Additionally, Global Asymmetric Semantic Fusion is proposed to integrate multi-scale features, enriching high-level feature representation. HDRUnet3d achieves state-of-the-art performance on different datasets, demonstrating the effectiveness and robustness of the proposed method.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117428"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417511","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

MLANet: Multilevel aggregation network for binocular eye-fixation prediction MLANet：双目注视预测的多级聚合网络

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-11-09 DOI: 10.1016/j.image.2025.117434

Wujie Zhou , Jiabao Ma , Yulai Zhang , Lu Yu , Weijia Gao , Ting Luo

{"title":"MLANet: Multilevel aggregation network for binocular eye-fixation prediction","authors":"Wujie Zhou , Jiabao Ma , Yulai Zhang , Lu Yu , Weijia Gao , Ting Luo","doi":"10.1016/j.image.2025.117434","DOIUrl":"10.1016/j.image.2025.117434","url":null,"abstract":"<div><div>Saliency prediction is an underexplored but fundamental task in computer vision, especially for binocular images. For binocular images, saliency prediction aims to find the most visually distinctive parts to imitate the operation of the human visual system. Existing saliency prediction models do not utilize the extracted multilevel features adequately. In this paper, we introduce a novel saliency prediction framework called multilevel aggregation network (MLANet) to explicitly model the multilevel features of binocular images. We split the multilevel features into shallow and deep features using the ResNet-34 backbone. We consider the first three and last three stage features as shallow and deep features, respectively for saliency prediction reconstruction. First, we introduce the aggregation module to integrate adjacent shallow and deep features. Thereafter, we utilize shallow aggregation and multiscale-aware modules to locate objects of different sizes with features from adjacent levels. For better reconstruction, we integrate deep- and shallow-level features with the help of feature guidance and deploy dual-attention modules to select the discriminative enhanced characteristics. Experimental results on two benchmark datasets, NCTU (CC of 0.8575, KLDiv of 0.2648, AUC of 0.8856, and NSS of 2.0138) and S3D (CC of 0.7977, KLDiv of 0.2024, AUC of 0.7954, and NSS of 1.2963), for binocular eye-fixation prediction show that the proposed MLANet outperforms the other compared methods.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117434"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520106","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

Integrated multi-channel approach for speckle noise reduction in SAR imagery using gradient, spatial, and frequency analysis 基于梯度、空间和频率分析的综合多通道SAR图像散斑降噪方法

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-09-23 DOI: 10.1016/j.image.2025.117406

Anirban Saha, Harshit Singh, Suman Kumar Maji

{"title":"Integrated multi-channel approach for speckle noise reduction in SAR imagery using gradient, spatial, and frequency analysis","authors":"Anirban Saha, Harshit Singh, Suman Kumar Maji","doi":"10.1016/j.image.2025.117406","DOIUrl":"10.1016/j.image.2025.117406","url":null,"abstract":"<div><div>Synthetic Aperture Radar (SAR) imagery is inherently marred by speckle noise, which undermines image quality and complicates subsequent analytical endeavors. While numerous strategies have been suggested in existing literature to mitigate this unwanted noise, the challenge of eliminating speckle while conserving subtle structural and textural details inherent in the raw data remains unresolved. In this article, we propose a comprehensive approach combining multi-domain analysis with gradient information processing for SAR. Our method aims to effectively suppress speckle noise while retaining crucial image characteristics. By leveraging multi-domain analysis techniques, we exploit both spatial and frequency domain information to gain a deeper insight into image structures. Additionally, we introduce a novel gradient information processing step that utilizes local gradient attributes to guide the process. Experimental results obtained from synthetic and real SAR imagery illustrate the effectiveness of our approach in terms of speckle noise reduction and preservation of image features. Quantitative assessments demonstrate substantial enhancements in image quality, indicating superior performance compared to current state-of-the-art methods.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117406"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159985","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 framework for realizing fraction-order filters with robust performance 一种实现分数阶滤波器鲁棒性的新框架

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-10-06 DOI: 10.1016/j.image.2025.117411

Yiguang Liu

{"title":"A new framework for realizing fraction-order filters with robust performance","authors":"Yiguang Liu","doi":"10.1016/j.image.2025.117411","DOIUrl":"10.1016/j.image.2025.117411","url":null,"abstract":"<div><div>In signal or image fields, how to design the filters not only revealing the high frequency information (such as image edges) but also subduing noise influence concurrently, is very important. To construct the filters having this performance, a new framework is given to realize filters with fractional orders. From the framework, a filter with <span><math><mrow><mi>m</mi><mo>+</mo><mn>1</mn></mrow></math></span> entries, actually performs <span><math><mrow><mo>(</mo><mi>m</mi><mi>α</mi><mo>)</mo></mrow></math></span>-order difference with <span><math><mi>α</mi></math></span> a complex number. When the absolute value of the real part of <span><math><mi>α</mi></math></span> is larger than a threshold, some entry of the filter is almost 2-periodic variable, and the others are close to zero. This sheds light on the fact, why a finite long filter cannot perform filtering with too high orders. By virtue of heuristic ways, an applicable restriction for the real part of <span><math><mi>α</mi></math></span> is given, <span><math><mrow><mo>[</mo><mo>−</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>]</mo></mrow></math></span>. Experimental results on synthetic data sequences indicate that the newly defined filters can disclose the local extreme points robustly while the Laplacian of Gaussian (LoG) operator completely fails, and the results on image benchmarks demonstrate that, the newly given filters can filter out more useful information details such as detecting edges while subduing noises, than the conventional integer-order difference filters such as LoG and Laplacian (Lap) operator. It is hopeful that more effective and practical filters can be constructed by the new framework.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117411"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321927","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 LDCT image denoising model based on dual-path attention 一种基于双路径关注的LDCT图像去噪模型

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-10-09 DOI: 10.1016/j.image.2025.117412

Xiaodong Guo , Xiaoyan Chang , Lifang Wang , Rongguo Zhang , Lihua Hu

{"title":"An LDCT image denoising model based on dual-path attention","authors":"Xiaodong Guo , Xiaoyan Chang , Lifang Wang , Rongguo Zhang , Lihua Hu","doi":"10.1016/j.image.2025.117412","DOIUrl":"10.1016/j.image.2025.117412","url":null,"abstract":"<div><div>Existing denoising methods for low-dose computed tomography (LDCT) images generally suffer from an imbalance between global correlation capture and local detail preservation, leading to blurring of the global image structure or loss of edge textures, which seriously impairs the accuracy of clinical diagnosis. To address this limitation, a dual-path attention denoising model is proposed. An LSTM-based attention module (LBAM) is introduced: it captures long-range sequence dependencies through a gating mechanism, focuses on key diagnostic information using residual connections, and enhances the modeling of cross-regional continuous morphology in the sequence dimension. Meanwhile, a multi-scale attention module (MSAM) is embedded in the U-Net architecture, which performs parallel modeling via channel-spatial attention and self-attention to achieve collaborative optimization of local detail enhancement and global correlation capture. The results show that the method can effectively balance the integrity of the global structure and the clarity of local details, significantly improving the quality of LDCT images. The PSNR and SSIM values in the Mayo dataset were 31.6725 and 0.8820, respectively, indicating the effectiveness of the method in removing noise and preserving image details.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117412"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321928","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

Ad-MNet with FConv: FPGA-enabled advanced MobileNet model with fast convolution accelerator for image resolution and quality enhancement Ad-MNet with FConv：支持fpga的高级MobileNet模型，具有快速卷积加速器，可提高图像分辨率和质量

IF 2.7 3区工程技术

Signal Processing-Image Communication Pub Date : 2026-01-01 Epub Date: 2025-11-07 DOI: 10.1016/j.image.2025.117433

L. Malathi

{"title":"Ad-MNet with FConv: FPGA-enabled advanced MobileNet model with fast convolution accelerator for image resolution and quality enhancement","authors":"L. Malathi","doi":"10.1016/j.image.2025.117433","DOIUrl":"10.1016/j.image.2025.117433","url":null,"abstract":"<div><div>In recent decades, image processing professionals have focused a great deal of emphasis on single image super-resolution (SISR), which attempts to reconstruct a high-resolution (HR) image from a low-resolution (LR) image. Particularly, deep learning-based super-resolution (SR) methods have attracted a lot of interest and significantly enhanced reconstruction performance on synthetic data. However, their applications are limited to real-world SR with resource-constrained devices because of their huge number of convolutions and parameters. This requires significant computational costs and memory storage for SR model training. To tackle these issues, an improved deep learning model is developed with the use of the fast convolution (FConv) accelerator and hybrid multiplier architecture. Before performing the quality enhancement procedure, the adaptive Fast Normalized Least Mean Square algorithm-based filtering method is initially applied to perform the denoising process. Then, the Advanced MobileNet (Ad-MNet) model with FConv accelerator is proposed to improve the image quality as well as resolution for recovering lost information during image acquisition. Also, a Hybrid Parallel Adder-based Multiplier (HPAM) is designed to perform the multiplication operation in FConv to speed up the convolution operation. The proposed accelerator is executed using MATLAB and Xilinx Verilog tools, and the performance analysis is done for different metrics. The study of the results shows that the accuracy of the proposed model is 98 % for the image resolution process and 98.9 % for the image enhancement process.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"140 ","pages":"Article 117433"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568191","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