IET Image Process.最新文献_第2页

Smart pansharpening approach using kernel-based image filtering 使用基于核的图像滤波的智能泛锐化方法

IET Image Process. Pub Date : 2021-05-18 DOI: 10.1049/IPR2.12251

Ahmad AL Smadi, Shuyuan Yang, Atif Mehmood, A. Abugabah, Min Wang, M. Bashir

引用次数: 6

An unsupervised person re-identification approach based on cross-view distribution alignment 一种基于交叉视图分布对齐的无监督人再识别方法

IET Image Process. Pub Date : 2021-05-13 DOI: 10.1049/IPR2.12256

Xibin Jia, Xing Wang, Qinggai Mi

{"title":"An unsupervised person re-identification approach based on cross-view distribution alignment","authors":"Xibin Jia, Xing Wang, Qinggai Mi","doi":"10.1049/IPR2.12256","DOIUrl":"https://doi.org/10.1049/IPR2.12256","url":null,"abstract":"Unsupervised clustering is a kind of popular solution for unsupervised person re-identiﬁcation (re-ID). However, due to the inﬂuence of cross-view differences, the results of clustering labels are not accurate. To solve this problem, an unsupervised re ID method based on cross-view distributed alignment (CV-DA) to reduce the inﬂuence of unsupervised cross-view is proposed. Speciﬁcally, based on a popular unsupervised clustering method, density clustering DBSCAN is used to obtain pseudo labels. By calculating the similarity scores of images in the target domain and the source domain, the similarity distribution of different camera views is obtained and is aligned with the distribution with the consistency constraint of pseudo labels. The cross-view distribution alignment constraint is used to guide the clustering process to obtain a more reliable pseudo label. The comprehensive comparative experiments are done in two public datasets, i.e. Market-1501 and DukeMTMC-reID. The comparative results show that the proposed method outper-forms several state-of-the-art approaches with mAP reaching 52.6% and rank1 71.1%. In order to prove the effectiveness of the proposed CV-DA, the proposed constraint is added into two advanced re-ID methods. The experimental results demonstrate that the mAP and rank increase by ∽ 0.5–2% after using the cross-view distribution alignment constraint comparing with that of the associated original methods without using CV-DA.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83129320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

HNSF Log-Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features HNSF Log-Demons:使用分层邻域光谱特征的差分同胚恶魔配准

IET Image Process. Pub Date : 2021-05-13 DOI: 10.1049/IPR2.12254

Xiaogang Du, Dongxin Gu, Tao Lei, Song Wang, Xuejun Zhang, H. Meng

引用次数: 1

Reversible data hiding for encrypted image based on adaptive prediction error coding 基于自适应预测误差编码的加密图像可逆数据隐藏

IET Image Process. Pub Date : 2021-05-11 DOI: 10.1049/IPR2.12252

Zhenjun Tang, M. Pang, Chunqiang Yu, Guijin Fan, Xianquan Zhang

{"title":"Reversible data hiding for encrypted image based on adaptive prediction error coding","authors":"Zhenjun Tang, M. Pang, Chunqiang Yu, Guijin Fan, Xianquan Zhang","doi":"10.1049/IPR2.12252","DOIUrl":"https://doi.org/10.1049/IPR2.12252","url":null,"abstract":"Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block-based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efﬁciently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS-2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in embedding capacity.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88688516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Thigh muscle segmentation using a hybrid FRFCM-based multi-atlas method and morphology-based interpolation algorithm 基于frfcm的多图谱方法和基于形态的插值算法的大腿肌肉分割

IET Image Process. Pub Date : 2021-05-07 DOI: 10.1049/IPR2.12245

Malihe Molaie, R. Zoroofi

{"title":"Thigh muscle segmentation using a hybrid FRFCM-based multi-atlas method and morphology-based interpolation algorithm","authors":"Malihe Molaie, R. Zoroofi","doi":"10.1049/IPR2.12245","DOIUrl":"https://doi.org/10.1049/IPR2.12245","url":null,"abstract":"The volume of lower extremity muscles is affected by some diseases. Quantiﬁcation of thigh muscles in medical images can lead to an easier investigation of these diseases. Most of the previous works in thigh muscle segmentation are based on models and atlases that require manually segmented datasets in 3D. As manual segmentation of these muscles is a time-consuming task, in this work, only one initial slice is segmented by a new hybrid FRFCM-based multi-atlas method and other slices are segmented based on this slice. In the proposed method, after noise reduction, the muscle region is extracted from other tissues by the FRFCM method. Then, an initial slice of each dataset is segmented by a multi-atlas method. The segmented muscles in the initial slice are used to segment muscles in the other slices of each dataset. The proposed method was evaluated with 20 CT datasets. The average DSC, Precision, and Sensitivity of the method for individual muscle segmentation were 91 . 20 ± 2 . 37, 91 . 95 ± 3 . 54, and 90 . 71 ± 3 . 89, respectively. The quantitative and intuitive results of the proposed method show the better results of this method in comparison to other state-of-the-art thigh muscle segmentation techniques.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82608376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A discriminative self-attention cycle GAN for face super-resolution and recognition 一种用于人脸超分辨和识别的判别自注意周期GAN

IET Image Process. Pub Date : 2021-05-06 DOI: 10.1049/IPR2.12250

Xiaoguang Li, Ning Dong, Jianglu Huang, L. Zhuo, Jiafeng Li

{"title":"A discriminative self-attention cycle GAN for face super-resolution and recognition","authors":"Xiaoguang Li, Ning Dong, Jianglu Huang, L. Zhuo, Jiafeng Li","doi":"10.1049/IPR2.12250","DOIUrl":"https://doi.org/10.1049/IPR2.12250","url":null,"abstract":"Face image captured via surveillance videos in an open environment is usually of low quality, which seriously affects the visual quality and recognition accuracy. Most image super-resolution methods adopt paired high-quality and its interpolated low-resolution version to train the super-resolution network. It is difﬁcult to achieve contented visual quality and restoring discriminative features in real scenarios. A discriminative self-attention cycle generative adversarial network is proposed for real-world face image super-resolution. Based on the cycle GAN framework, unpaired samples are adopted to train a degradation network and a reconstruction network simultaneously. A self-attention mechanism is employed to capture the contextual information for details restoring. A Siamese face recognition network is introduced to provide a constraint on identify consistency. In addition, an asymmetric perceptual loss is introduced to handle the imbalance between the degradation model and the reconstruction model. Experimental results show that the observation model achieved more realistic low-quality face images, and the super-resolved face images have shown better subjective quality and higher face recognition performance.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84700063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Blood vessel and background separation for retinal image quality assessment 血管和背景分离用于视网膜图像质量评价

IET Image Process. Pub Date : 2021-05-04 DOI: 10.1049/IPR2.12244

Yipeng Liu, Yajun Lv, Zhanqing Li, Jing Li, Yan Liu, Peng Chen, Ronghua Liang

{"title":"Blood vessel and background separation for retinal image quality assessment","authors":"Yipeng Liu, Yajun Lv, Zhanqing Li, Jing Li, Yan Liu, Peng Chen, Ronghua Liang","doi":"10.1049/IPR2.12244","DOIUrl":"https://doi.org/10.1049/IPR2.12244","url":null,"abstract":"Retinal image analysis has become an intuitive and standard aided diagnostic technique for eye diseases. The good image quality is essential support for doctors to provide timely and accurate disease diagnosis. This paper proposes an end-to-end learning based method for evaluating the retinal image quality. First, blood vessels of the input image are segmented by U-Net, and the fundus image is divided into two parts: blood vessels and background. Then, we design a dual branch network module which extracts global features that inﬂuence the image quality and suppress the interference of blood vessels and local textures to achieve better performance. The proposed module can be embedded in various advanced network structures. The experimental results show the more efﬁcient convergence rate for the network with the module. The best network accuracy rate is 85.83%, the AUC is 0.9296, and the F1-score is 0.7967 on the collected local dataset. Additionally, the model generalization is tested on the public DRIMDB dataset. The accuracy, AUC, and F1-score reach 97.89%, 0.9978, and 0.9688, respectively. Compared with the state-of-the-art networks, the performance of the proposed method is proven to be accurate and effective for retinal image quality assessment.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82094382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Medical image steganographic algorithm via modified LSB method and chaotic map 基于改进LSB方法和混沌映射的医学图像隐写算法

IET Image Process. Pub Date : 2021-05-03 DOI: 10.1049/IPR2.12246

A. Karawia

{"title":"Medical image steganographic algorithm via modified LSB method and chaotic map","authors":"A. Karawia","doi":"10.1049/IPR2.12246","DOIUrl":"https://doi.org/10.1049/IPR2.12246","url":null,"abstract":"Many methods of hiding information in an image are existing now. The least signiﬁcant bit is the famous method used in steganographic algorithms. Medical image steganography is a technique used to make the transmission of these images secure so that the decision of the Specialist physician based on these images is not affected. In this paper, medical image steganographic algorithm using modiﬁed least signiﬁcant bit and chaotic map is proposed. The main problem is that the selection of embedding pixels within the host image is not protected enough in most existing methods. So, the author used two-dimensional piecewise smooth chaotic map to select the positions of these pixels randomly. On the other hand, all bits in the secret medical image are transmitted without losing any bit. To do that, the secret medical image is encrypted using one-dimensional piecewise chaotic map (Tent map). Then, the steganographic algorithm is used to hide the bits of the encrypted secret medical image. The bits of each embedded pixel are shufﬂed before the embedding pro-cess randomly. After that, the stego image is created. The host image and stego image are analysed with the peak signal-to-noise ratio, the mean square error, histogram test, image quality measure and relative entropy test. The stego image displays acceptable result when comparing with the host image. Also, the chi-square attack test is performed and the stego image can resist it. The proposed algorithm can assist the sending of medical images via communication media.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81008802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Underwater image enhancement based on colour correction and fusion 基于色彩校正和融合的水下图像增强

IET Image Process. Pub Date : 2021-05-02 DOI: 10.1049/IPR2.12247

Daqi Zhu, Zhiqiang Liu, Youmin Zhang

引用次数: 6

A method of lining seam elimination with angle adaptation and rectangular mark for road tunnel concrete lining images 一种基于角度自适应和矩形标记的道路隧道混凝土衬砌图像衬砌缝消除方法

IET Image Process. Pub Date : 2021-03-24 DOI: 10.1049/IPR2.12177

Zhong-si Qu, Y. Zhong, Ling Liu

{"title":"A method of lining seam elimination with angle adaptation and rectangular mark for road tunnel concrete lining images","authors":"Zhong-si Qu, Y. Zhong, Ling Liu","doi":"10.1049/IPR2.12177","DOIUrl":"https://doi.org/10.1049/IPR2.12177","url":null,"abstract":"Road Tunnels are an important part of the current road transportation infrastructure. As the main form of tunnel lining diseases, cracks are easy to interact with other areas, which seriously affects the safe operation of the tunnel. Due to the similarity of brightness and linearity between surface cracks and lining cracks, the existing crack detection algorithms can not extract cracks accurately and quickly. An algorithm of lining seam crack elimination with rectangular mark is proposed here. First, the line segments in the image are detected by the Line Segment Detector algorithm based on the coarse percolation detection of the crack. Second, the distribution directions are calculated, and cracks from the lining seams are distinguished by the adaptive threshold judgment method. Third, by using the distribution characteristics of pixels, the line segments are extended to form rectangular marks perpendicular to the direction of lining seams. Finally, the marking information is used to remove the lining joints and obtain the real surface cracks of tunnel lining. Experimental results show that the algorithm can quickly and effectively remove any shape distribution of lining seam. The algorithm ﬁlls in the of concrete tunnel lining surface crack","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72587899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1