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A Real-Time Imaging processing Method Based on Modified RMA with Sub-Aperture Images Fusion for Spaceborne Spotlight SAR

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A Real-Time Imaging Processing Method Based on Modified RMA ...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fang Zhou Jun Yang Guangcai Sun Jiajia Zhang School of Computer and Information Hefei University of Technology Hefei China National Key Lab of Radar Signal Processing Xidian University Xi'an China Key Laboratory of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

The small satellite SAR has received increasing attention due to its flexibility and low cost. But limited by the data transmission technology, real-time transmission of a large amount of raw data generated by the spaceborne spotlight SAR can hardly be achieved. Meanwhile, the azimuth bandwidth of the spotlight mode is larger than the PRF, resulting in aliasing of the azimuth spectrum. Based on these, this paper proposes a real-time scheme for small satellite SAR with spotlight mode. The method can solve the problem of data transmission and eliminate spectrum overlap in Doppler domain by means of sub-aperture processing. The modified range migration algorithm (RMA) is used to perform range compression and range cell migration compensation (RCMC) on sub-aperture data. Then dechirp in the azimuth time domain is applied to obtain the low-resolution complex image focused in the range time-azimuth frequency domain. Finally, all theected onto a grid image with azimuth interval matching the azimuth full-resolution to complete image fusion.

关键词： Azimuth Spaceborne radar Real-time systems Synthetic aperture radar Doppler effect Imaging Bandwidth

New Framework for Code-Mapping-based Reversible Data Hiding in JPEG Images

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arXiv 2020年

作者： Du, Yang Yin, Zhaoxia Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Anhui Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Communication & Electronic Engineering East China Normal University Shanghai200241 China

Code mapping (CM) is an efficient technique for reversible data hiding (RDH) in JPEG images, which embeds data by constructing a mapping relationship between the used and unused codes in the JPEG bitstream. This study presents a new framework for designing a CM-based RDH method. First, a new code mapping strategy is proposed to suppress file size expansion and improve applicability. Based on our proposed strategy, the mapped codes are redefined by creating a new Huffman table rather than selecting them from the unused codes in the original Huffman table. The critical issue of designing the CM-based RDH method, that is, constructing code mapping, is converted into a combinatorial optimization problem. This study proposes a novel CM-based RDH method that utilizes a genetic algorithm (GA). The experimental results demonstrate that the proposed method achieves a high embedding capacity with no signal distortion while suppressing file size expansion. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping

Separable reversible data hiding based on MSB prediction for encrypted 3D mesh models?

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arXiv 2019年

作者： Xu, Na Yin, Zhaoxia Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei

Reversible data hiding in encrypted domain (RDH-ED) has received tremendous attention from the research community because data can be embedded into cover media without exposing it to the third party data hider and the cover media can be losslessly recovered after the extraction of the embedded data. Although, in recent years, exten- sive studies have been carried out about images based RDH-ED, little attention is paid to RDH-ED in 3D mesh models due to its complex data structure and irregular geometry. We propose a separable RDH-ED method for 3D mesh models based on MSB (most significant bit) predic- tion. Firstly, we divide all the vertices of the mesh into the "embedded" set and "reference" set according to topological information between ver- tices, and calculate the MSB prediction errors for the vertices of the "em- bedded" set. Then, bit-stream encryption is used to encrypt the mesh. Finally, data is embedded by replacing the MSB of the encrypted vertex coordinates of the "embedded" set by the additional data bits. Recipient with data hiding key can correctly extract the additional data from the marked encrypted mesh. Recipient with encryption key can recover the mesh perfectly by using MSB prediction. Experimental results show that the proposed method has higher embedding capacity and higher quality of the recovered meshes compared to the existing RDH-ED method for 3D meshes based on symmetric encryption. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter

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Research on Biomedical Engineering 2020年第2期36卷 107-119页

作者： dos Santos, Jucelino Cardoso Marciano Carrijo, Gilberto Arantes de Fátima dos Santos Cardoso, Cristiane Ferreira, Júlio César Sousa, Pedro Moises Patrocínio, Ana Cláudia Signal Processing Lab Faculty of Electrical Engineering Federal University of Uberlândia Campus Sta Mônica Av. João Naves de Ávila 2121 Bloco 1N UberlândiaMGCEP 38400-000 Brazil Computer Vision Lab Informatics Nucleo Goiano Federal Institute of Education Science and Technology Campus Urutaí Rodovia Geraldo Silva Nascimento km 2.5 Zona Rural Bloco de Informática UrutaíGOCEP 75790-000 Brazil Biomedical Lab Faculty of Electrical Engineering Federal University of Uberlândia Campus Sta Mônica Av. João Naves de Ávila 2121 Bloco 3N UberlândiaMGCEP 38400-000 Brazil

Purpose: Blood vessel segmentation is the most important step for detecting changes in retinal vascular structures in retinal images. While these images are widely used in clinical diagnosis, they are generally degraded by noise and are limited by low contrast. In this paper, we address the problem of improving fundus image quality for blood vessel detection. Methods: We used contrast limited adaptive histogram equalization (CLAHE) to improve contrast and the Wiener filter for noise reduction. A multilayer artificial neural network was used to optimize the values from CLAHE and the Wiener filter for blood vessel segmentation. Furthermore, several training and classification rounds were performed (3240, with 200 epochs each), using a combination of CLAHE and Wiener parameters and a fixed network configuration. Results: The proposed methodology was tested in the DRIVE database, achieving accuracy, sensitivity, and specificity values of 0.9505, 0.7564, and 0.9696, respectively. Conclusion: The results were encouraging for almost all metrics and comparable to those of state-of-the-art blood vessel segmentation processes. Therefore, the parameter set effectively improved the fundus image quality for blood vessel segmentation, relative to the classification. These results are important since the more precise the segmentation step is, the greater the chances are of building a robust and specialized diagnostic system. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Image quality

Comments on "design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model"

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arXiv 2020年

作者： Wahab, Abdul Khan, Shujaat Khan, Farrukh Zeeshan Department of Mathematics School of Sciences and Humanities Nazarbayev University 53 Kabanbay Batyr Avenue Nur-Sultan010000 Kazakhstan Bio-Imaging Signal Processing and Learning Lab. Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Computer Science University of Engineering and Technology Taxila Taxila47080 Pakistan

The purpose of this article is to discuss some aspects of the convergence analysis performed in the paper [Design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model, Eur. Phys. J. Plus (2019) 134: 407]. It is highlighted that the way the authors prove convergence suffers a lack of correct and valid mathematical justifications. Copyright © 2020, The Authors. All rights reserved.

关键词： Momentum

Feedback alfa-rooting algorithm for medical image enhancement 17

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Feedback alfa-rooting algorithm for medical image enhancemen...

17th Image processing: Algorithms and Systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology "STANKIN" Moscow Russia CUNY/ College of Staten Island Dept. of Computer Science New York United States

This paper presents a new combined local and global transform domain-based feedback image enhancement algorithm for medical diagnosis, treatment, and clinical research. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. The block size and alfa-rooting parameters driven through optimization using the Agaian's cost function (image enhancement non-reference quality measure). The presented new approach allows enhancing MRI and CT images with uneven lighting and brightness gradient by preserving the local image features/details. Extensive computer simulations (CS) on real medical images are offered to gage the presented method. CS shows that our method improves the contrast and enhances the details of the medical images effectively compared with the current state-of-art methods. © 2019, Society for Imaging Science and technology.

关键词： Clinical research

Multiple reconstruction compression framework based on PNG image

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arXiv 2019年

作者： Lu, Zhiqing Yin, Zhaoxia Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, this paper proposes a joint framework based on neural network and zoom compression. The framework first encodes the incoming PNG or JPEG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next we import the intermediate state image into the zooming compressor and re-pressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system can not feel the change of the effect. Copyright © 2019, The Authors. All rights reserved.

关键词： Image compression

The quaternion-based anisotropic gradient for the color images 17

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The quaternion-based anisotropic gradient for the color imag...

17th Image processing: Algorithms and Systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology"STANKIN" Moscow Russia CUNY College of Staten Island Dept. of Computer Science New York United States

Image gradient, as a preprocessing step is an essential tool in image processing in many research areas such as edge detection, segmentation, smoothing, inpainting, etc. In the present paper, we develop a new gradient by integrated the quaternion framework with LPA-ICI (local polynomial approximation - the intersection of confidence intervals) based on anisotropic gradient concepts for the color image processing applications. computer simulations on the Berkeley Segmentation Dataset show that the new quaternion anisotropic gradient exhibits fewer color artifacts compared to state-of-the-art techniques including well-knowledge gradient operators. © 2019, Society for Imaging Science and technology.

关键词： Polynomial approximation

Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration

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arXiv 2020年

作者： Oliari, Vinícius Goossens, Sebastiaan Häger, Christian Liga, Gabriele Bütler, Rick M. van den Hout, Menno van der Heide, Sjoerd Pfister, Henry D. Okonkwo, Chigo Alvarado, Alex Information and Communication Theory Lab Signal Processing Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Electrical and Computer Engineering Duke University Durham United States High Capacity Optical Transmission Lab Electro-Optical Communication Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands

Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch". One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems. In this paper, we challenge this assumption and show how to carefully design multi-step approaches that provide better performance–complexity tradeoffs than their few-step counterparts. We consider the recently proposed learned digital backpropagation (LDBP) approach, where the linear steps in the split-step method are re-interpreted as general linear functions, similar to the weight matrices in a deep neural network. Our main contribution lies in an experimental demonstration of this approach for a 25 Gbaud single-channel optical transmission system. It is shown how LDBP can be integrated into a coherent receiver DSP chain and successfully trained in the presence of various hardware impairments. Our results show that LDBP with limited complexity can achieve better performance than standard DBP by using very short, but jointly optimized, finite-impulse response filters in each step. This paper also provides an overview of recently proposed extensions of LDBP and we comment on potentially interesting avenues for future work. Copyright © 2020, The Authors. All rights reserved.

关键词： Light transmission