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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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Learning Sparsifying Transforms for image Reconstruction in Electrical Impedance Tomography

Learning Sparsifying Transforms for Image Reconstruction in ...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Kaiyi Yang Narong Borijindargoon Boon Poh Ng Saiprasad Ravishankar Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University Singapore Michigan State University USA

ISBN: (纸本)9781728176055;9781728176062

Electrical Impedance Tomography (EIT) is a fast and non-invasive imaging technology that reconstructs the internal electrical properties of a subject. However, its functionality is limited by low spatial resolution arising from an ill-posed and ill-conditioned inverse problem. Several sparsity-promoting regularization methods have been applied to improve the quality of EIT image reconstruction, including various l_0 and l_1-based analytical models (TV, TwIST, etc.), and a patch-based sparse representation via a learned dictionary (using the K-SVD algorithm), dubbed CS-EIT. To further exploit the potential of compressed sensing in Electrical Impedance Tomography, this paper incorporates the recent novel method of transform learning for EIT image reconstruction. We propose a blind compressed sensing algorithm, dubbed TL-EIT, which simultaneously optimizes the sparsifying transform and updates the reconstructed image. We demonstrate using both synthetic and in vivo data that the proposed TL-EIT is more effective than other sparsity-based algorithms for reconstructing high-quality EIT images. In addition, TL-EIT also accelerates the reconstruction process in comparison to other learning-based algorithms like CS-EIT.

关键词： Electrical impedance tomography In vivo TV signal processing algorithms Transforms signal processing Speech processing

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Research on Haze Traffic signal image processing Based on Retinex Algorithm

Research on Haze Traffic Signal Image Processing Based on Re...

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ICMLCA 2021;2nd international conference on Machine Learning and Computer Application

作者： Lijuan Qin Xinyu He Tao Liu

The signal lamp is affected by the haze weather conditions, and the color of the signal lamp image taken will be disturbed to a certain extent. Before color threshold segmentation, it is necessary to restore and enhance the color of the signal lamp to a certain extent, so as to ensure the accuracy and real-time performance of color segmentation and subsequent recognition. Retinex algorithm is an image enhancement theory based on human vision system. In recent years, it has been widely studied and applied. In this paper, SSR, MSR and MSRCR algorithms are introduced in detail, and these three methods are applied to haze traffic signal image enhancement. The experimental results of image processing are given, and the images have been enhanced to different degrees, and the experimental results are good. Traffic signal processing and recognition is one of the key technologies and difficulties of intelligent vehicle unmanned driving, so the research of this paper has important practical application value.

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A Critical Review on Water Level Measurement Techniques for Flood Mitigation

A Critical Review on Water Level Measurement Techniques for ...

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IEEE international conference on signal and image processing Applications (ICSIPA)

作者： Obaid Rafiq Jan Hudyjaya Siswoyo Jo Riady Siswoyo Jo Swinburne University of Technology Sarawak Kuching Malaysia

Flood related disasters have been the cause of millions of misplaced people and casualties worldwide. Hence it is necessary to develop monitoring systems capable of measuring water levels and depths by means of sensors or camera surveillance. Contact measurement sensors have disadvantages of regular maintenance and high operational costs, whereas camera surveillance combined with computer vision and image processing outweighs the advantages of the former measurement systems. In this study, traditional image processing and some more advanced and current state-of-the-art computer vision techniques have been compared and analyzed according to their implementation.

关键词： Computer vision image processing Surveillance Current measurement Maintenance engineering Cameras Sensor systems

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A Consensus Equilibrium Solution For Deep image Prior Powered By Red

A Consensus Equilibrium Solution For Deep Image Prior Powere...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Rakib Hyder Hassan Mansour Yanting Ma Petros T. Boufounos Pu Wang University of California Riverside CA Mitsubishi Electric Research Laboratories Cambridge MA

ISBN: (纸本)9781728176055;9781728176062

Recent advances in solving imaging inverse problems have witnessed the combination of deep learning models with classical image models for better signal representation. One such approach, DeepRED, combines the deep image prior (DIP) with the regularization by denoising (RED) framework to boost the performance of image deblurring and super resolution tasks. In this paper, we formulate DeepRED as a consensus equilibrium problem and set up a fixed-point algorithm for solving the equilibrium equations. We also derive sufficient conditions that the DIP generative prior should satisfy to ensure that the corresponding fixed-point operator is non-expansive. We then demonstrate that the fixed-point algorithm that solves the CE equations results in improved image reconstruction quality in a deblurring setting compared to state-of-the-art methods.

关键词： Sufficient conditions Inverse problems Noise reduction signal processing algorithms Mathematical model signal representation Task analysis

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Restoration Medical images from Speckle Noise Using Multifilters

Restoration Medical Images from Speckle Noise Using Multifil...

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international conference on advanced Computing and Communication Systems (ICACCS)

作者： Maha Abdulameer Kadhim Middle Technical University (MTU) Baghdad Iraq

In digital image processing and precisely in the medical field, most of the medical images are usually corrupted by different types of noises which give importance to achieve high-quality images to facilitate the observation operations for the given medical applications. The techniques of de-noising have become a basic practice in the applications of medical imaging to study of processing and biological structure of the medical images, which cause to be the processes of restoring the medical images that have a wide challenge in the scope of medical image processing. Therefore, to overcome these challenges, many restore algorithms have been developed like, Gaussian, median, and Weiner filters. These filters are used for the most generally affected noises in the medical images such as Gaussian, Speckle, Salt and Pepper, and Poisson noises, etc. This paper focuses on a process of adding noise using the speckle noise of the medical image and removes this noise by using three types of filters as a mean, median, and Weiner filters, these filters applied in different stages on the different medical image.

关键词： Ultrasonic imaging Noise reduction Medical services Speckle Filtering algorithms Biology image restoration

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Smart Phone Camera based Weighing Scale for Kitchens in Household Applications 1

Smart Phone Camera based Weighing Scale for Kitchens in Hous...

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1st international conference on Advances in Smart Sensor, signal processing and Communication Technology, ICASSCT 2021

作者： Patil, Vaishali S. Shinde, Santosh A. Dhawale, Nandkishor M. Department of Electronics Shivaji University Maharashtra Kolhapur416004 India Department of Instrumentation Engineering P.V.P. Institute of Technology Maharashtra Budhgaon Sangli416304 India

Measurement of physical variables is the most important task in manufacturing, production and trading of each and every produce. E.g. our clothes cannot be stitched properly without taking bodily measurements by a tailor. Household furniture if produced oversize cannot be brought inside homes if the measurement's of door at entry was not considered in prior. Kitchen food cooked by every fellow Indian sisters, mothers, and daughters can produce some mouthwatering tastes, only if all cooking ingredients were mixed in the right proportion's, else many have experienced the odds, with special attention to salt, sugar, chili powder, turmeric powder, and other spices. Cooking food is an art and science both and the ingredients to be added in proportion are measured in prior using tools such as measuring spoons, cups and sometimes using weighing scales. Often measurements are carried with confidence of eyes and hands without use of tools, with special attention if one does not have a measuring tool. Not many have access to measuring scales in our society. However it is estimated that 60 % of people in India have access to Smart Phones. Modern Smart Phones are built using at least a 10's of sensors, among which the Camera is the most popular. A modest weight measuring device can be developed based on very simple algorithms from the wide spectrum of image processing tools. This paper attempts to discuss the use of smart phones to quantify (estimate) the weight of at least one cooking ingredient used in every household. The same device can be used to estimate to identify the quality of the same ingredient in terms of size, color, nutrition, etc. © Published under licence by IOP Publishing Ltd.

关键词： image processing

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Laplacian Regularized Tensor Low-Rank Minimization for Hyperspectral Snapshot Compressive Imaging

Laplacian Regularized Tensor Low-Rank Minimization for Hyper...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Yi Yang Fei Jiang Hongtao Lu Shanghai Jiao Tong University China

ISBN: (纸本)9781728176055;9781728176062

Snapshot Compressive Imaging (SCI) systems, including hyperspectral compressive imaging and video compressive imaging, are designed to depict high-dimensional signals with limited data by mapping multiple images into one. One key module of SCI systems is a high quality reconstruction algorithm for original frames. However, most existing decoding algorithms are based on vectorization representation and fail to capture the intrinsic structural information of high dimensional signals. In this paper, we propose a tensor-based low-rank reconstruction algorithm with hyper-Laplacian constraint for hyperspectral SCI systems. First, we integrate the non-local self-similarity and tensor low-rank minimization approach to explore the intrinsic structural correlations along spatial and spectral domains. Then, we introduce a hyper-Laplacian constraint to model the global spectral structures, alleviating the ringing artifacts in the spatial domain. Experimental results on hyperspectral image corpus demonstrate the proposed algorithm achieves average 0.8～2.9 dB improvement in PSNR over state-of-the-art work.

关键词： image coding Tensors Correlation Imaging signal processing algorithms Reconstruction algorithms Minimization

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A multispectral image based object detection approach in natural scene

A multispectral image based object detection approach in nat...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Chuanyuan Zhao Xiangjuan Li School of Computer Xi’an Shiyou University Xi’an China

ISBN: (纸本)9781665447300

In this paper, we investigate the feasibility of blueberry detection based on multispectral image, and build robust classification models, which tolerate outdoor illumination changes and complicated background information. In the fruit growth, there are three stages, mature, near mature, and young. Base on the multispectral image and the normalized vegetation index NDVI, we construct a classifier to detect the different growth stage of the fruits by analyzing the color component and using the C4.5 algorithm. The experimental results show that the correct recognition rate of the mature, approximate mature and immature fruit are 74.58%, 79.91% and 86.52%, respectively, which demonstrate that the proposed classifier has a better recognition effect for immature fruits.

关键词： Reflectivity image recognition image color analysis Vegetation mapping signal processing algorithms Lighting Object detection

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Analysis of DNN based image watermarking data generation for self-recovery

Analysis of DNN based image watermarking data generation for...

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Control, Automation, Power and signal processing (CAPS), 2021 international conference on Control, Automation, Power and signal processing (CAPS)

作者： Siddharth Bhalerao Irshad Ahmad Ansari Anil Kumar Discipline of Electronics and Communication Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (纸本)9781665445788

The world is rapidly going digital and data security is the foremost concern. Digital images form a large chunk of this digital data. Therefore, techniques like digital image watermarking are used for assuring image security. In this paper application of DNN based compression algorithm is examined for implementation of color image tampering detection and self-recovery system. The DNN based image compression is used to compress the host images and multiple copies of compressed images are embedded in the host image for self-recovery. Due to the smaller size and better quality provided by DNN based compression algorithm, the watermarked images have high PSNR (peak signal to noise ratio). Similarly, the quality of recovered images is also high. The DNN based algorithm is analyzed for different images and tampering conditions. In the best-case scenario, the algorithm can recover the image from about 88% tampering.

关键词： image coding PSNR Digital images Data security signal processing algorithms Watermarking Color

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A Robust Autofocus Algorithm for Stripmap SAR

A Robust Autofocus Algorithm for Stripmap SAR

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IEEE international conference on signal and image processing (ICSIP)

作者： Tao Li Yaowen Fu Jianfeng Zhang Wenpeng Zhang Wei Yang College of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9781665446006

Multi-subaperture autofocus algorithm is a commonly-used autofocus algorithm for processing stripmap SAR data. However, when the phase error is large, the performance of traditional multi-subaperture autofocus algorithms may decline. A robust multi-subaperture autofocus algorithm for stripmap SAR is proposed in this paper. This algorithm estimates the error in subapertures based on the minimum-entropy criterion first, and then use a "coarse to fine" method to estimate the shift amount of the error gradients. To speed up the algorithm, a variable-step-size search method is used. The real data processing verifies the feasibility and robustness of the algorithm.

关键词： Splicing Search methods image processing conferences Estimation Data processing Robustness

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