Signal Processing-Image Communication最新文献_第4页

Image super-resolution based on multifractals in transfer domain

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2025-01-03 DOI: 10.1016/j.image.2024.117221

Xunxiang Yao, Qiang Wub, Peng Zhange, Fangxun Baod

{"title":"Image super-resolution based on multifractals in transfer domain","authors":"Xunxiang Yao, Qiang Wub, Peng Zhange, Fangxun Baod","doi":"10.1016/j.image.2024.117221","DOIUrl":"10.1016/j.image.2024.117221","url":null,"abstract":"<div><div>The goal of image super-resolution technique is to reconstruct high-resolution image with fine texture details from its low-resolution version.On Fourier domain,such fine details are more related to the information in the highfrequency spectrum. Most of existing methods do not have specific modules to handle such high-frequency information adaptively. Thus, they cause edge blur or texture disorder. To tackle the problems, this work explores image super-resolution on multiple sub-bands of the corresponding image, which are generated by NonSubsampled Contourlet Transform (NSCT). Different sub-bands hold the information of different frequency which is then related to the detailedness of information of the given low-resolution image.In this work, such image information detailedness is formulated as image roughness. Moreover, fractals analysis is applied to each sub-band image. Since fractals can mathematically represent the image roughness, it then is able to represent the detailedness (i.e. various frequency of image information). Overall, a multi-fractals formulation is established based on multiple sub-bands image. On each sub-band, different fractals representation is created adaptively. In this way, the image super-resolution process is transformed into a multifractal optimization problem. The experiment result demonstrates the effectiveness of the proposed method in recovering high-frequency details.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"133 ","pages":"Article 117221"},"PeriodicalIF":3.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420492","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

Middle-output deep image prior for blind hyperspectral and multispectral image fusion

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2024-12-26 DOI: 10.1016/j.image.2024.117247

Jorge Bacca , Christian Arcos , Juan Marcos Ramírez , Henry Arguello

{"title":"Middle-output deep image prior for blind hyperspectral and multispectral image fusion","authors":"Jorge Bacca , Christian Arcos , Juan Marcos Ramírez , Henry Arguello","doi":"10.1016/j.image.2024.117247","DOIUrl":"10.1016/j.image.2024.117247","url":null,"abstract":"<div><div>Obtaining a low-spatial-resolution hyperspectral image (HS) or low-spectral-resolution multispectral (MS) image from a high-resolution (HR) spectral image is straightforward with knowledge of the acquisition models. However, the reverse process, from HS and MS to HR, is an ill-posed problem known as spectral image fusion. Although recent fusion techniques based on supervised deep learning have shown promising results, these methods require large training datasets involving expensive acquisition costs and long training times. In contrast, unsupervised HS and MS image fusion methods have emerged as an alternative to data demand issues; however, they rely on the knowledge of the linear degradation models for optimal performance. To overcome these challenges, we propose the Middle-Output Deep Image Prior (MODIP) for unsupervised blind HS and MS image fusion. MODIP is adjusted for the HS and MS images, and the HR fused image is estimated at an intermediate layer within the network. The architecture comprises two convolutional networks that reconstruct the HR spectral image from HS and MS inputs, along with two networks that appropriately downscale the estimated HR image to match the available MS and HS images, learning the non-linear degradation models. The network parameters of MODIP are jointly and iteratively adjusted by minimizing a proposed loss function. This approach can handle scenarios where the degradation operators are unknown or partially estimated. To evaluate the performance of MODIP, we test the fusion approach on three simulated spectral image datasets (Pavia University, Salinas Valley, and CAVE) and a real dataset obtained through a testbed implementation in an optical lab. Extensive simulations demonstrate that MODIP outperforms other unsupervised model-based image fusion methods by up to 6 dB in PNSR.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"132 ","pages":"Article 117247"},"PeriodicalIF":3.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148378","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

AggNet: Learning to aggregate faces for group membership verification

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2024-12-02 DOI: 10.1016/j.image.2024.117237

Marzieh Gheisari , Javad Amirian , Teddy Furon , Laurent Amsaleg

引用次数: 0

Multi-granular inter-frame relation exploration and global residual embedding for video-based person re-identification

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2024-11-30 DOI: 10.1016/j.image.2024.117240

Zhiqin Zhu , Sixin Chen , Guanqiu Qi , Huafeng Li , Xinbo Gao

{"title":"Multi-granular inter-frame relation exploration and global residual embedding for video-based person re-identification","authors":"Zhiqin Zhu , Sixin Chen , Guanqiu Qi , Huafeng Li , Xinbo Gao","doi":"10.1016/j.image.2024.117240","DOIUrl":"10.1016/j.image.2024.117240","url":null,"abstract":"<div><div>In recent years, the field of video-based person re-identification (re-ID) has conducted in-depth research on how to effectively utilize spatiotemporal clues, which has attracted attention for its potential in providing comprehensive view representations of pedestrians. However, although the discriminability and correlation of spatiotemporal features are often studied, the exploration of the complex relationships between these features has been relatively neglected. Especially when dealing with multi-granularity features, how to depict the different spatial representations of the same person under different perspectives becomes a challenge. To address this challenge, this paper proposes a multi-granularity inter-frame relationship exploration and global residual embedding network specifically designed to solve the above problems. This method successfully extracts more comprehensive and discriminative feature representations by deeply exploring the interactions and global differences between multi-granularity features. Specifically, by simulating the dynamic relationship of different granularity features in long video sequences and using a structured perceptual adjacency matrix to synthesize spatiotemporal information, cross-granularity information is effectively integrated into individual features. In addition, by introducing a residual learning mechanism, this method can also guide the diversified development of global features and reduce the negative impacts caused by factors such as occlusion. Experimental results verify the effectiveness of this method on three mainstream benchmark datasets, significantly surpassing state-of-the-art solutions. This shows that this paper successfully solves the challenging problem of how to accurately identify and utilize the complex relationships between multi-granularity spatiotemporal features in video-based person re-ID.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"132 ","pages":"Article 117240"},"PeriodicalIF":3.4,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148374","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

GAN-based multi-view video coding with spatio-temporal EPI reconstruction

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2024-11-29 DOI: 10.1016/j.image.2024.117242

Chengdong Lan, Hao Yan, Cheng Luo, Tiesong Zhao

{"title":"GAN-based multi-view video coding with spatio-temporal EPI reconstruction","authors":"Chengdong Lan, Hao Yan, Cheng Luo, Tiesong Zhao","doi":"10.1016/j.image.2024.117242","DOIUrl":"10.1016/j.image.2024.117242","url":null,"abstract":"<div><div>The introduction of multiple viewpoints in video scenes inevitably increases the bitrates required for storage and transmission. To reduce bitrates, researchers have developed methods to skip intermediate viewpoints during compression and delivery, and ultimately reconstruct them using Side Information (SInfo). Typically, depth maps are used to construct SInfo. However, these methods suffer from reconstruction inaccuracies and inherently high bitrates. In this paper, we propose a novel multi-view video coding method that leverages the image generation capabilities of Generative Adversarial Network (GAN) to improve the reconstruction accuracy of SInfo. Additionally, we consider incorporating information from adjacent temporal and spatial viewpoints to further reduce SInfo redundancy. At the encoder, we construct a spatio-temporal Epipolar Plane Image (EPI) and further utilize a convolutional network to extract the latent code of a GAN as SInfo. At the decoder, we combine the SInfo and adjacent viewpoints to reconstruct intermediate views using the GAN generator. Specifically, we establish a joint encoder constraint for reconstruction cost and SInfo entropy to achieve an optimal trade-off between reconstruction quality and bitrate overhead. Experiments demonstrate the significant improvement in Rate–Distortion (RD) performance compared to state-of-the-art methods.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"132 ","pages":"Article 117242"},"PeriodicalIF":3.4,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148373","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

Multi-layer feature fusion based image style transfer with arbitrary text condition

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2024-11-28 DOI: 10.1016/j.image.2024.117243

Yue Yu, Jingshuo Xing, Nengli Li

引用次数: 0

Adaptive histogram equalization framework based on new visual prior and optimization model

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2024-11-28 DOI: 10.1016/j.image.2024.117246

Shiqi Liu, Qiding Lu, Shengkui Dai

{"title":"Adaptive histogram equalization framework based on new visual prior and optimization model","authors":"Shiqi Liu, Qiding Lu, Shengkui Dai","doi":"10.1016/j.image.2024.117246","DOIUrl":"10.1016/j.image.2024.117246","url":null,"abstract":"<div><div>Histogram Equalization (HE) algorithm remains one of the research hotspots in the field of image enhancement due to its computational simplicity. Despite numerous improvements made to HE algorithms, few can comprehensively account for all major drawbacks of HE. To address this issue, this paper proposes a novel histogram equalization framework, which is an adaptive and systematic resolution. Firstly, a novel optimization mathematical model is proposed to seek the optimal controlling parameters for modifying the histogram. Additionally, a new visual prior knowledge, termed Narrow Dynamic Prior (NDP), is summarized, which describes and reveals the subjective perceptual characteristics of the Human Visual System (HVS) for some special types of images. Then, this new knowledge is organically integrated with the new model to expand the application scope of HE. Lastly, unlike common brightness preservation algorithms, a novel method for brightness estimation and precise control is proposed. Experimental results demonstrate that the proposed equalization framework significantly mitigates the major drawbacks of HE, achieving notable advancements in striking a balance between contrast, brightness and detail of the output image. Both objective evaluation metrics and subjective visual perception indicate that the proposed algorithm outperforms other excellent competition algorithms selected in this paper.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"132 ","pages":"Article 117246"},"PeriodicalIF":3.4,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148375","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

Visual information fidelity based frame level rate control for H.265/HEVC 基于视觉信息保真度的H.265/HEVC帧级速率控制

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2024-11-28 DOI: 10.1016/j.image.2024.117245

Luheng Jia , Haoqiang Ren , Zuhai Zhang , Li Song , Kebin Jia

{"title":"Visual information fidelity based frame level rate control for H.265/HEVC","authors":"Luheng Jia , Haoqiang Ren , Zuhai Zhang , Li Song , Kebin Jia","doi":"10.1016/j.image.2024.117245","DOIUrl":"10.1016/j.image.2024.117245","url":null,"abstract":"<div><div>Rate control in video coding seeks for various trade-off between bitrate and reconstruction quality, which is closely tied to image quality assessment. The widely used measurement of mean squared error (MSE) is inadequate in describing human visual characteristics, therefore, rate control algorithms based on MSE often fail to deliver optimal visual quality. To address this issue, we propose a frame level rate control algorithm based on a simplified version of visual information fidelity (VIF) as the quality assessment criterion to improve coding efficiency. Firstly, we simplify the VIF and establish its relationship with MSE, which reduce the computational complexity to make it possible for VIF to be used in video coding framework. Then we establish the relationship between VIF-based <span><math><mi>λ</mi></math></span> and MSE-based <span><math><mi>λ</mi></math></span> for <span><math><mi>λ</mi></math></span>-domain rate control including bit allocation and parameter adjustment. Moreover, using VIF-based <span><math><mi>λ</mi></math></span> directly integrates VIF-based distortion into the MSE-based rate–distortion optimized coding framework. Experimental results demonstrate that the coding efficiency of the proposed method outperforms the default frame-level rate control algorithms under distortion metrics of PSNR, SSIM, and VMAF by 3.4<span><math><mtext>%</mtext></math></span>, 4.0<span><math><mtext>%</mtext></math></span> and 3.3<span><math><mtext>%</mtext></math></span> in average. Furthermore, the proposed method reduces the quality fluctuation of the reconstructed video at high bitrate range and improves the bitrate accuracy under hierarchical configuration .</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"131 ","pages":"Article 117245"},"PeriodicalIF":3.4,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759483","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

Transformer-based multiview spatiotemporal feature interactive fusion for human action recognition in depth videos 基于变换的多视点时空特征交互融合深度视频人体动作识别

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2024-11-23 DOI: 10.1016/j.image.2024.117244

Hanbo Wu, Xin Ma, Yibin Li

{"title":"Transformer-based multiview spatiotemporal feature interactive fusion for human action recognition in depth videos","authors":"Hanbo Wu, Xin Ma, Yibin Li","doi":"10.1016/j.image.2024.117244","DOIUrl":"10.1016/j.image.2024.117244","url":null,"abstract":"<div><div>Spatiotemporal feature modeling is the key to human action recognition task. Multiview data is helpful in acquiring numerous clues to improve the robustness and accuracy of feature description. However, multiview action recognition has not been well explored yet. Most existing methods perform action recognition only from a single view, which leads to the limited performance. Depth data is insensitive to illumination and color variations and offers significant advantages by providing reliable 3D geometric information of the human body. In this study, we concentrate on action recognition from depth videos and introduce a transformer-based framework for the interactive fusion of multiview spatiotemporal features, facilitating effective action recognition through deep integration of multiview information. Specifically, the proposed framework consists of intra-view spatiotemporal feature modeling (ISTFM) and cross-view feature interactive fusion (CFIF). Firstly, we project a depth video into three orthogonal views to construct multiview depth dynamic volumes that describe the 3D spatiotemporal evolution of human actions. ISTFM takes multiview depth dynamic volumes as input to extract spatiotemporal features of three views with 3D CNN, then applies self-attention mechanism in transformer to model global context dependency within each view. CFIF subsequently extends self-attention into cross-attention to conduct deep interaction between different views, and further integrates cross-view features together to generate a multiview joint feature representation. Our proposed method is tested on two large-scale RGBD datasets by extensive experiments to demonstrate the remarkable improvement for enhancing the recognition performance.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"131 ","pages":"Article 117244"},"PeriodicalIF":3.4,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745549","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

Vocal cord anomaly detection based on Local Fine-Grained Contour Features 基于局部精细轮廓特征的声带异常检测

IF 3.4 3区工程技术

Signal Processing-Image Communication Pub Date : 2024-11-02 DOI: 10.1016/j.image.2024.117225

Yuqi Fan , Han Ye , Xiaohui Yuan

{"title":"Vocal cord anomaly detection based on Local Fine-Grained Contour Features","authors":"Yuqi Fan , Han Ye , Xiaohui Yuan","doi":"10.1016/j.image.2024.117225","DOIUrl":"10.1016/j.image.2024.117225","url":null,"abstract":"<div><div>Laryngoscopy is a popular examination for vocal cord disease diagnosis. The conventional screening of laryngoscopic images is labor-intensive and depends heavily on the experience of the medical specialists. Automatic detection of vocal cord diseases from laryngoscopic images is highly sought to assist regular image reading. In laryngoscopic images, the symptoms of vocal cord diseases are concentrated in the inner vocal cord contour, which is often characterized as vegetation and small protuberances. The existing classification methods pay little, if any, attention to the role of vocal cord contour in the diagnosis of vocal cord diseases and fail to effectively capture the fine-grained features. In this paper, we propose a novel Local Fine-grained Contour Feature extraction method for vocal cord anomaly detection. Our proposed method consists of four stages: image segmentation to obtain the overall vocal cord contour, inner vocal cord contour isolation to obtain the inner contour curve by comparing the changes of adjacent pixel values, extraction of the latent feature in the inner vocal cord contour by taking the tangent inclination angle of each point on the contour as the latent feature, and the classification module. Our experimental results demonstrate that the proposed method improves the detection performance of vocal cord anomaly and achieves an accuracy of 97.21%.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"131 ","pages":"Article 117225"},"PeriodicalIF":3.4,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700767","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