Block Compressed Sensing Based on Image Complexity

2012 ◽  
Vol 157-158 ◽  
pp. 1287-1292
Author(s):  
Yu Ming Cao ◽  
Yan Feng ◽  
Ying Biao Jia ◽  
Chang Sheng Dou
Keyword(s):  
Compressed Sensing ◽  
Traditional Method ◽  
Recovery Process ◽  
Data Sampling ◽  
Independent Measurement ◽  
Image Complexity ◽  
Image Characteristics ◽  
Block Compressed Sensing ◽  
Recovery Performance ◽  
A New Technique

Compressed sensing (CS) is a new Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. Inspired by recent theoretical advances in compressive sensing, we propose a new CS algorithm which takes the image complexity into consideration. Image will be divided into small blocks, and then acquisition is conducted in a block-by-block manner. Each block has independent measurement and recovery process. The extraordinary thought proposed is that we sufficiently take advantage of image characteristics in measurement process, which make our measurement more effective and efficient. Experimental results tell that our algorithm has better recovery performance than traditional method, and its calculation amount has greatly reduced.

Hard Thresholding Pursuit with Partially Known Support for Compressed Sensing

2013 ◽  
Vol 718-720 ◽  
pp. 669-674 ◽  
Author(s):  
Rui Wu ◽  
Wei Huang
Keyword(s):  
Theoretical Analysis ◽  
Compressed Sensing ◽  
Prior Information ◽  
Recovery Process ◽  
Restricted Isometry Property ◽  
Hard Thresholding ◽  
Simulation Experiments ◽  
Recovery Performance ◽  
Support Information

Compressed sensing has attracted lots of interest in recent years. Recent works in modified compressed sensing exploited the prior information about the signal to reduce the number of measurements. In this paper, we propose a hard thresholding pursuit algorithm with partially known support (HTP-PKS), which incorporates the prior support information into the recovery process. Theoretical analysis shows that by using prior information of partially known support, the HTP-PKS algorithm presents stable and robust recovery performance under a relaxed restricted isometry property (RIP) condition. To illustrate, simulation experiments are given.

Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

2020 ◽  
Vol 16 (3) ◽  
pp. 262-272
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Hongwei Sun
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Linear Array ◽  
Imaging System ◽  
Ultrasonic Imaging ◽  
Large Data ◽  
Reconstruction Algorithm ◽  
Array Transducer ◽  
Total Data ◽  
Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

Remote sensing images fusion based on block compressed sensing

2013 ◽  
Author(s):  
Sen-lin Yang ◽  
Guo-bin Wan ◽  
Bian-lian Zhang ◽  
Xin Chong
Keyword(s):  
Remote Sensing ◽  
Compressed Sensing ◽  
Remote Sensing Images ◽  
Block Compressed Sensing

scholarly journals Block Compressed Sensing of Images Using Adaptive Granular Reconstruction

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Ran Li ◽  
Hongbing Liu ◽  
Yu Zeng ◽  
Yanling Li
Keyword(s):  
Computational Complexity ◽  
Compressed Sensing ◽  
Structural Characteristic ◽  
Rate Distortion ◽  
Reconstructed Image ◽  
Hard Thresholding ◽  
Low Computational Complexity ◽  
Principle Components Analysis ◽  
Reconstruction Performance ◽  
Block Compressed Sensing

In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.

scholarly journals Reversible Data Hiding in Block Compressed Sensing Images

ETRI Journal ◽  
2016 ◽  
Vol 38 (1) ◽  
pp. 159-163 ◽  
Author(s):  
Ming Li ◽  
Di Xiao ◽  
Yushu Zhang
Keyword(s):  
Compressed Sensing ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Block Compressed Sensing

Multi-Scale Block Compressed Sensing Algorithm Based on Gray-Level Co-Occurrence Matrix

2021 ◽  
Vol 58 (4) ◽  
pp. 0410002
Author(s):  
李金凤 Li Jinfeng ◽  
赵雨童 Zhao Yutong ◽  
黄纬然 Huang Weiran ◽  
郭巾男 Guo Jinnan
Keyword(s):  
Compressed Sensing ◽  
Gray Level ◽  
Multi Scale ◽  
Block Compressed Sensing ◽  
Occurrence Matrix

Compressed Sensing and Its Application in CT and EEG

2017 ◽  
pp. 1126-1149
Author(s):  
Sajib Saha ◽  
Murat Tahtali
Keyword(s):  
Compressed Sensing ◽  
Applied Mathematics ◽  
Signal Reconstruction ◽  
Optimization Technique ◽  
Linear Measurements ◽  
Reconstruction Procedure ◽  
Sparsity Constraints ◽  
Ill Posed ◽  
X Ray Computed ◽  
A New Technique

Compressed sensing, also known as compressive sampling is a new technique being rapidly developed over the last few years. The theory states that when some prior information about the signal is available and appropriately incorporated into the signal reconstruction procedure, a signal can be accurately reconstructed even if the Shannon/ Nyquest sampling requirement is violated. The key idea of compressed sensing is to recover a sparse signal from very few non-adaptive, linear measurements by optimization technique. Following the discovery by Donoho in (2006), that sparsity could enable exact solution of ill-posed problems under certain conditions, there has been a tremendous growth on efficient application of sparsity constraints for solving ill-posed problems. The theoretical foundation of compressed sensing has already become a key concept in various areas of applied mathematics, computer science, and electrical engineering. In this chapter we will detail the application of compressed sensing in X-ray computed tomography (CT) and Electroencephalography. Starting from the very basic principles we will provide theoretical justifications on why and how sparsity prior is used in CT and in EEG.

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