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A class of nonlinear parabolic systems having standard growth and L¹ data

NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS 2022年第0期67卷

作者： Atlas, Abdelghafour Karami, Fahd Meskine, Driss Ouakrim, Youssef Oubbih, Omar Univ Cadi Ayyad Ecole Natl Sci Appl Marrakech BP 575 Ave Abdelkrim Khattabi Marrakech Morocco Univ Cadi Ayyad Ecole Superieurede Technol Essaouira Lab Math Informat Modelisat Syst Complexes BP 383 Essaouira Jadida Essaouira Morocco Univ Sidi Mohamed Ben Abdellah Ecole Normale Super Fes Lab Math Modelisat Phys Appliquee Fez city Morocco

In this paper, we are interested in the mathematical and numerical study of a reaction-diffusion system arising in the PDE-constrained optimization problems and image processing. The proposed model extends the recent work (Afraites et al., 2016) by taking L1 data. We prove the existence and uniqueness of entropy solutions on the Orlicz-Sobolev space. Then, we present numerical experiments to illustrate the performance of the L1 data choice and we discuss its efficiency compared to other image denoising algorithms. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： image denoising Staircase effect Nonlinear inverse problems Nonlinear diffusion Entropy solution Orlicz-Sobolev space

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Watermark Detection and Clearance in Video Using Simple Signal and image processing Techniques 27

Watermark Detection and Clearance in Video Using Simple Sign...

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27th International Conference on systems, Signals and image processing (IWSSIP)

作者： Gonzalez Montero, Jose Ramon Diaz, Alberto Reyes Rosillo, Egberto Caballero Conci, Aura Fed Fluminense Univ Comp Inst BR-24210310 Niteroi RJ Brazil

ISBN: (纸本)9781728175393

A digital watermark is an information or signal inserted into a digital content (image, audio, video, document, software) that can be used to determine its ownership, showing who created or sold it, or to ensure its integrity. Typically, the same watermark is added consistently to mark movie collections. Consequently, media with a visible watermark will be eventually susceptible to the necessity of watermark remission, operation that could be an issue. This paper presents two new and very fast algorithms for watermark detection and removal. The here considered watermarks could be blended, overlaid or added in a continuous form to the digital video on remission treatment. The first of such algorithms use a template matching approach to pre-process the blended watermarks frame-to-frame in the Hue-Saturation-Value (HSV) color space, and then improve the detection using a post-processing step. The second algorithm presents two steps as well. Initially, it estimates the structure of a translucent watermark observing the constancy of its gradient magnitude in the area corresponding to the tenth part of each video. Secondly, it uses a sliding window to detect the watermarked in the frames. The results show that watermark detection by both algorithms has high sensitivity. Moreover, they are very fast when compared with other methods especially those based on deep learning.

关键词： watermark steganography digital signal image processing gradient sliding windows

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image aesthetic assessment based on emotion-assisted multi-task learning network 21

Image aesthetic assessment based on emotion-assisted multi-t...

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6th International Conference on Multimedia systems and Signal processing, ICMSSP 2021

作者： Chen, Yunlong Pu, Yuanyuan Zhao, Zhengpeng Xu, Dan Man Qian, Wenhua School of Information Yunnan University China School of Information Yunnan University Key Laboratory of Technology and Application of Internet of Things in Colleges Universities of Yunnan Province China

ISBN: (纸本)9781450390378

image emotion recognition and image aesthetic assessment are recent research hotspots in user perception of image content. However, for the study of image aesthetics and image emotion, the vast majority of studies are separated from the relationship between the two, respectively. But in fact, there is a potential and explicit connection between image aesthetics and image emotion. In this paper, we use a multi-task convolutional neural network. To achieve the purpose of this experiment, for the first time, we propose a strictly labeled dataset, which contains multi-person annotated aesthetic scores and eight types of emotion distributions. With the help of this dataset, on the basis of aesthetic assessment based on scene and object branch, the method of an emotion-assisted multi-task learning network is adopted to improve the performance of aesthetic assessment. The experimental results show that our network structure achieves excellent performance in many indexes of image aesthetic assessment when comparing several state-of-the-art aesthetic assessment algorithms and convolution neural networks with strong performance. © 2021 ACM.

关键词： Convolutional neural networks

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Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological images 27

Analysis of Features for Breast Cancer Recognition in Differ...

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27th International Conference on systems, Signals and image processing (IWSSIP)

作者： Carvalho, Rafael H. de O. Martins, Alessandro S. Neves, Leandro A. do Nascimento, Marcelo Z. Univ Fed Uberlandia Fac Comp Sci Uberlandia MG Brazil Fed Inst Triangulo Mineiro Ituiutaba Brazil Sao Paulo State Univ Dept Comp Sci & Stat Sao Jose Do Rio Preto Brazil

ISBN: (纸本)9781728175393

Breast cancer is one of the most common diseases in women in the world. There are various imaging techniques employed in the diagnosis. The histological image analysis supported by computational systems has proved to be quite effective in diagnosing the disease. In this paper, we present an approach to quantify and classify tissue samples of the breast based on features extracted from the intensity histogram, co-occurrence matrix and the Shannon, Renyi, Tsallis and Kapoor entropies. The attribute set was employed to obtain the feature vectors which were evaluated as inputs to the random forest and sequential minimal optimization algorithms with the 10-fold cross-validation technique. In this study, we investigated the proposed approach with images obtained in four levels of magnification of the publicly available Breast Cancer Histopathological Database. In the feature selection stage, we investigated the correlation-Based feature selection, ReliefF, information gain, gain ratio, one-R and symmetrical uncertainty algorithms for evaluating the performance of the proposed approach. The proposed approach achieved significant results of AUC and accuracy for all cases analyzed. The proposed approach obtained 0.997 for AUC and 97.6% for the accuracy metric. These results are considered relevant and this approach is useful as an automated protocol for the diagnosis of breast histological tissue.

关键词： Breast cancer Entropy CAD Histological image Feature Extraction

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Blind CT image Quality Assessment Model Based on CT image Statistics 4

Blind CT Image Quality Assessment Model Based on CT Image St...

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4th International Symposium on image Computing and Digital Medicine, ISICDM 2020

作者： Duan, Jiayu Cai, Jianmei Zhi, Shaohua Mou, Xuanqin Xi'an Jiaotong University School of Information and Communication Engineering China

ISBN: (纸本)9781450389686

image quality assessment is widely used in many image processing tasks, which can help researchers adjust image processing algorithms, design imaging systems, and evaluate image processing systems. Generally, CT image quality assessment can be categorized into task-specific and general image quality evaluation. Task-specific image quality assessment evaluates the performance of the imaging system or the detectability of the tumor. These IQA index, for example, are modulation transfer function (MTF), Signal-to-Noise Ratio (SNR), observer model, etc. General image quality assessment measures the general reconstruction image quality under different reconstruction algorithms. SSIM (Structural Similarity), Mean Squared Error (MSE), etc. are the traditional general image quality assessment indexes widely used in nowadays CT image quality assessment. The drawback of these indexes is the demand for reference images, which is not practical in the real CT system. In this paper, we design a CT image dataset, and by using this dataset, and we propose a blind image quality assessment (BIQA) model based on CT image statistics, which can be employed to measure the algorithms under no reference image situation. Different from other image datasets, we recruited no-converged images of the reconstruction process in designing datasets, which enables our BIQA model to evaluate non-converged images during the iterations. Hence, the BIQA model can be embedded in the reconstruction process to monitor reconstructed image quality during iterations. © 2020 ACM.

关键词： image quality

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Analysis and Design of image Encryption algorithms Based on Interlaced Chaos 10th

Analysis and Design of Image Encryption Algorithms Based on ...

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10th International Conference on Computer Engineering and Networks, CENet 2020

作者： Li, Kangman Li, Qiuping College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

For chaotic encryption algorithm, the algorithm of generating chaotic sequence is complex, and the data is float, which directly affects the speed of encryption. In this paper, an interlaced chaotic encryption algorithm is proposed, In this algorithm, the pixel interval is encrypted by chaotic sequence, and the chaotic sequences of other pixels are obtained by XOR the chaotic values of the front and back points, Experiments show that the algorithm can encrypt and decrypt images quickly and has good effect. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Multiple Robustness Enhancements for image Adaptive Steganography in Lossy Channels

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2020年第8期30卷 2750-2764页

作者： Zhang, Yi Luo, Xiangyang Guo, Yanqing Qin, Chuan Liu, Fenlin State Key Lab Math Engn & Adv Comp Zhengzhou 450002 Peoples R China Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

Considering that traditional image steganography technologies suffer from the potential risk of failure under lossy channels, an enhanced adaptive steganography with multiple robustness against image processing attacks is proposed, while maintaining good detection resistance. First, a robust domain constructing method is proposed utilizing robust element extraction and optimal element modification, which can be applied to both spatial and JPEG images. Then, a robust steganography is proposed based on "Robust Domain Constructing + RS-STC Codes," combined with cover selection, robust cover extraction, message coding, and embedding with minimized costs. In addition, to provide a theoretical basis for message extraction integrity, the fault tolerance of the proposed algorithm is deduced using error model based on burst errors and decoding damage. Finally, on the basis of parameter discussion about robust domain construction, performance experiments are conducted, and the recommended coding parameters are given for lossy channels with different attacks using the analytic results for fault tolerance. A series of experimental results demonstrate that the proposed algorithm can extract embedded messages with significantly higher accuracy after different attacks, such as compression, noising, scaling and other attacks, compared with the state-of-the-art adaptive steganography, and robust watermarking algorithms, while maintaining good detection resistant performance.

关键词： image coding Resistance Robustness Transform coding Discrete cosine transforms Feature extraction Robust adaptive steganography image processing attacks statistical detection robust domain construction RS-STC codes

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A Fully Reversible Data Hiding Scheme in Encrypted images Based on Homomorphic Encryption and Pixel Prediction

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CIRCUITS systems AND SIGNAL processing 2020年第7期39卷 3532-3552页

作者： Liu, Jianyi Zhao, Kaifeng Zhang, Ru Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China

Reversible data hiding in encrypted images (RDHEI) is a widely used technique for privacy protection, which has been developed in many applications that require high confidentiality, authentication and integrity. Proposed RDHEI methods do not allow high embedding rate while ensuring losslessly recover the original image. Moreover, embedding computational complexity and embedding rate are not satisfactory when using public key encryption-based methods. This paper proposes a new fully reversible method based on Paillier homomorphic cryptosystem and pixel prediction. We use gradient adjust prediction to obtain redundant space in the original image and embed secret information with homomorphic operation after encryption. Experimental results show that our scheme can achieve high capacity (near 0.95 bpp) and can losslessly recover the original images with peak signal to noise ratio of +infinity and structural similarity of 1. Compared with proposed reversible data hiding algorithms, our scheme has a significant improvement in reconstructing quality, safety and capacity.

关键词： Reversible data hiding image encryption Homomorphic cryptosystem Pixel prediction image recovery

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An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

An Optimizing Parameters and Feature Selection in SVM Based ...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

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Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworks

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 106卷 534-544页

作者： Serrano, Estefania Garcia-Blas, Javier Carretero, Jesus Desco, Manuel Abella, Monica Univ Carlos III Madrid Comp Architecture & Technol Grp Madrid Spain Inst Invest Sanitaria Gregorio Maranon Madrid Spain Univ Carlos III Madrid Dept Bioingn & Ingn Aeroespacial Madrid Spain Ctr Nacl Invest Cardiovasculares Carlos III Madrid 28911 Spain Ctr Invest Biomed Red Salud Mental CIBERSAM Madrid Spain

One of the latest trends in Computed Tomography (CT) is the reduction of the radiation dose delivered to patients through the decrease of the amount of acquired data. This reduction results in artifacts in the final images if conventional reconstruction methods are used, making it advisable to employ iterative algorithms to enhance image quality. Most approaches are built around two main operators, backprojection and projection, which are computationally expensive. In this work, we present an implementation of those operators for iterative reconstruction methods exploiting the Big Data paradigm. We define an architecture based on Apache Spark that supports both Graphical processing Units (GPU) and CPU-based architectures. The aforementioned are parallelized using a partitioning scheme based on the division of the volume and irregular data structures in order to reduce the cost of communication and computation of the final images. Our solution accelerates the execution of the two most computational expensive components with Apache Spark, improving the programming experience of new iterative reconstruction algorithms and the maintainability of the source code increasing the level of abstraction for non-experienced high performance programmers. Through an experimental evaluation, we show that we can obtain results up to 10x faster for projection and 21x faster for backprojection when using a GPU-based cluster compared to a traditional multi-core version. Although a linear speed up was not reached, the proposed approach can be a good alternative for porting previous medical image reconstruction applications already implemented in C/C++ or even with CUDA or OpenCL programming models. Our solution enables the automatic detection of the GPU devices and execution on CPU and GPU tasks at the same time under the same system, using all the available resources. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Apache Spark GPU Medical image processing Computed tomography Iterative reconstruction algorithms

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