IET Image Process.最新文献

Mask-Guided Image Person Removal with Data Synthesis 基于数据合成的面具引导图像人物去除

IET Image Process. Pub Date : 2022-09-29 DOI: 10.2139/ssrn.4234905

Yunliang Jiang, Chenyang Gu, Zhenfeng Xue, Xiongtao Zhang, Yong Liu

引用次数: 1

EDAfuse: A encoder-decoder with atrous spatial pyramid network for infrared and visible image fusion EDAfuse:一种用于红外和可见光图像融合的编码器-解码器

IET Image Process. Pub Date : 2022-09-01 DOI: 10.2139/ssrn.3982278

Cairen Nie, Dongming Zhou, Rencan Nie

引用次数: 2

Visible part prediction and temporal calibration for pedestrian detection 行人检测的可见部分预测和时间标定

IET Image Process. Pub Date : 2022-08-30 DOI: 10.2139/ssrn.4072475

Peiyu Yang, Weixiang Li, Lu Wang, Lisheng Xu, Qingxu Deng

引用次数: 0

STDC-MA Network for Semantic Segmentation 语义分割的STDC-MA网络

IET Image Process. Pub Date : 2022-05-10 DOI: 10.48550/arXiv.2205.04639

Xiaochun Lei, Linjun Lu, Zetao Jiang, Zhaoting Gong, Chang Lu, Jiaming Liang

{"title":"STDC-MA Network for Semantic Segmentation","authors":"Xiaochun Lei, Linjun Lu, Zetao Jiang, Zhaoting Gong, Chang Lu, Jiaming Liang","doi":"10.48550/arXiv.2205.04639","DOIUrl":"https://doi.org/10.48550/arXiv.2205.04639","url":null,"abstract":"Semantic segmentation is applied extensively in autonomous driving and intelligent transportation with methods that highly demand spatial and semantic information. Here, an STDC-MA network is proposed to meet these demands. First, the STDC-Seg structure is employed in STDC-MA to ensure a lightweight and efficient structure. Subsequently, the feature alignment module (FAM) is applied to understand the offset between high-level and low-level features, solving the problem of pixel offset related to upsampling on the high-level feature map. Our approach implements the effective fusion between high-level features and low-level features. A hierarchical multiscale attention mechanism is adopted to reveal the relationship among attention regions from two different input sizes of one image. Through this relationship, regions receiving much attention are integrated into the segmentation results, thereby reducing the unfocused regions of the input image and improving the effective utilization of multiscale features. STDC- MA maintains the segmentation speed as an STDC-Seg network while improving the segmentation accuracy of small objects. STDC-MA was verified on the verification set of Cityscapes. The segmentation result of STDC-MA attained 76.81% mIOU with the input of 0.5x scale, 3.61% higher than STDC-Seg.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78055664","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

IET Image Process. Pub Date : 2022-03-17 DOI: 10.48550/arXiv.2203.09550

Xian Shi, Zhe Cui, Shaobing Zhang, Miao Cheng, L. He, Xianghong Tang

{"title":"Multi-similarity based Hyperrelation Network for few-shot segmentation","authors":"Xian Shi, Zhe Cui, Shaobing Zhang, Miao Cheng, L. He, Xianghong Tang","doi":"10.48550/arXiv.2203.09550","DOIUrl":"https://doi.org/10.48550/arXiv.2203.09550","url":null,"abstract":"Few-shot semantic segmentation aims at recognizing the object regions of unseen categories with only a few annotated examples as supervision. The key to few-shot segmentation is to establish a robust semantic relationship between the support and query images and to prevent overfitting. In this paper, we propose an effective Multi-similarity Hyperrelation Network (MSHNet) to tackle the few-shot semantic segmentation problem. In MSHNet, we propose a new Generative Prototype Similarity (GPS), which together with cosine similarity can establish a strong semantic relation between the support and query images. The locally generated prototype similarity based on global feature is logically complementary to the global cosine similarity based on local feature, and the relationship between the query image and the supported image can be expressed more comprehensively by using the two similarities simultaneously. In addition, we propose a Symmetric Merging Block (SMB) in MSHNet to efficiently merge multi-layer, multi-shot and multi-similarity hyperrelational features. MSHNet is built on the basis of similarity rather than specific category features, which can achieve more general unity and effectively reduce overfitting. On two benchmark semantic segmentation datasets Pascal-5i and COCO-20i, MSHNet achieves new state-of-the-art performances on 1-shot and 5-shot semantic segmentation tasks.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90647288","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 Screen-Shooting Resilient Document Image Watermarking Scheme using Deep Neural Network 一种基于深度神经网络的截屏弹性文档图像水印方案

IET Image Process. Pub Date : 2022-03-10 DOI: 10.48550/arXiv.2203.05198

Sulong Ge, Zhihua Xia, Yao Tong, Jian Weng, Jia-Nan Liu

{"title":"A Screen-Shooting Resilient Document Image Watermarking Scheme using Deep Neural Network","authors":"Sulong Ge, Zhihua Xia, Yao Tong, Jian Weng, Jia-Nan Liu","doi":"10.48550/arXiv.2203.05198","DOIUrl":"https://doi.org/10.48550/arXiv.2203.05198","url":null,"abstract":"With the advent of the screen-reading era, the confidential documents displayed on the screen can be easily captured by a camera without leaving any traces. Thus, this paper proposes a novel screen-shooting resilient watermarking scheme for document image using deep neural network. By applying this scheme, when the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the captured photographs. Specifically, our scheme is an end-to-end neural network with an encoder to embed watermark and a decoder to extract watermark. During the training process, a distortion layer between encoder and decoder is added to simulate the distortions introduced by screen-shooting process in real scenes, such as camera distortion, shooting distortion, light source distortion. Besides, an embedding strength adjustment strategy is designed to improve the visual quality of the watermarked image with little loss of extraction accuracy. The experimental results show that the scheme has higher robustness and visual quality than other three recent state-of-the-arts. Specially, even if the shooting distances and angles are in extreme, our scheme can also obtain high extraction accuracy.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75817191","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: 6

Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions 测地线格兰曼去噪在不同概率分布中被噪声污染图像中的应用

IET Image Process. Pub Date : 2022-03-04 DOI: 10.48550/arXiv.2203.02600

Yonggi Park, K. Gajamannage, Alexey L. Sadovski

引用次数: 1

Spectral recovery-guided hyperspectral super-resolution using transfer learning 使用迁移学习的光谱恢复引导的高光谱超分辨率

IET Image Process. Pub Date : 2021-05-20 DOI: 10.1049/IPR2.12253

Shaolei Zhang, Guangyuan Fu, Hongqiao Wang, Yuqing Zhao

{"title":"Spectral recovery-guided hyperspectral super-resolution using transfer learning","authors":"Shaolei Zhang, Guangyuan Fu, Hongqiao Wang, Yuqing Zhao","doi":"10.1049/IPR2.12253","DOIUrl":"https://doi.org/10.1049/IPR2.12253","url":null,"abstract":"Single hyperspectral image (HSI) super-resolution (SR) has attracted researcher’s attention; however, most existing methods directly model the mapping between low- and high-resolution images from an external training dataset, which requires large memory and com-puting resources. Moreover, there are few such available training datasets in real cases, which prevent deep-learning-based methods from further improving performance. Here, a novel single HSI SR method based on transfer learning is proposed. The proposed method is composed of two stages: spectral down-sampled image SR reconstruction based on transfer learning and HSI reconstruction via spectral recovery module. Instead of directly applying the learned knowledge from the colour image domain to HSI SR, the spectral down-sampled image is fed into a spatial SR model to obtain a high-resolution image, which acts as a bridge between the colour image and HSI. The spectral recovery network is used to restore the HSI from the bridge image. In addition, pre-training and collaborative ﬁne-tuning are proposed to promote the performance of SR and spectral recovery. Experiments on two public HSI datasets show that the proposed method achieves promising SR performance with a small paired HSI dataset.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74797409","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

Texture and exposure awareness based refill for HDRI reconstruction of saturated and occluded areas 基于纹理和曝光感知的HDRI饱和和闭塞区域重建

IET Image Process. Pub Date : 2021-05-19 DOI: 10.1049/IPR2.12257

Jianming Zhou, Yipeng Deng, Qin Liu, T. Ikenaga

引用次数: 0

An improved algorithm using weighted guided coefficient and union self-adaptive image enhancement for single image haze removal 基于加权引导系数和联合自适应图像增强的单幅图像去雾算法

IET Image Process. Pub Date : 2021-05-19 DOI: 10.1049/IPR2.12255

Guangbin Zhou, Lifeng He, Yong Qi, Meimei Yang, Xiao Zhao, Y. Chao

{"title":"An improved algorithm using weighted guided coefficient and union self-adaptive image enhancement for single image haze removal","authors":"Guangbin Zhou, Lifeng He, Yong Qi, Meimei Yang, Xiao Zhao, Y. Chao","doi":"10.1049/IPR2.12255","DOIUrl":"https://doi.org/10.1049/IPR2.12255","url":null,"abstract":"The visibility of outdoor images is usually signiﬁcantly degraded by haze. Existing dehazing algorithms, such as dark channel prior (DCP) and colour attenuation prior (CAP), have made great progress and are highly effective. However, they all suffer from the problems of dark distortion and detailed information loss. This paper proposes an improved algorithm for single-image haze removal based on dark channel prior with weighted guided coefﬁcient and union self-adaptive image enhancement. First, a weighted guided coefﬁcient method with sampling based on guided image ﬁltering is proposed to reﬁne the transmission map efﬁciently. Second, the k -means clustering method is adopted to calibrate the original image into bright and non-bright colour areas and form a transmission constraint matrix. The constraint matrix is then marked by connected-component labelling, and small bright regions are eliminated to form an atmospheric light constraint matrix, which can suppress the halo effect and optimize the atmospheric light. Finally, an adaptive linear contrast enhancement algorithm with a union score is proposed to optimize restored images. Experimental results demonstrate that the proposed algorithm can overcome the problems of image distortion and detailed information loss and is more efﬁcient than conventional dehazing algorithms.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87041728","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