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.

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.

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 Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images

2020 ◽  
Vol 6 (10) ◽  
pp. 103
Author(s):  
Ali S. Awad
Keyword(s):  
Gaussian Noise ◽  
Prior Information ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
The Other ◽  
Noisy Image ◽  
Simulation Experiments ◽  
Two Dimensional Image ◽  
Pixel Location ◽  
Image Pixels

In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other is external, based on the index or pixel location in the image. The proposed method focuses on leveraging these two types of prior information to obtain tangible results. To this end, very similar patches are collected from the noisy image. This is done by sorting the image pixels in ascending order and then placing them in consecutive rows in a new two-dimensional image. Henceforth, a principal component analysis is applied on the patch matrix to help remove the small noisy components. Since the restored pixels are similar or close in values to those in the clean image, it is preferable to arrange them using indices similar to those of the clean pixels. Simulation experiments show that outstanding results are achieved, compared to other known methods, either in terms of image visual quality or peak signal to noise ratio. Specifically, once the proper indices are used, the proposed method achieves PSNR value better than the other well-known methods by >1.5 dB in all the simulation experiments.

scholarly journals Parameter Research of UAV Vertical Rope-type Recovery System

2018 ◽  
Vol 179 ◽  
pp. 03001
Author(s):  
Chengjing Ma ◽  
Xing'an Liu ◽  
Caiwen Zhang
Keyword(s):  
Simulation Analysis ◽  
Initial Conditions ◽  
Recovery Process ◽  
Resistance Force ◽  
Recovery Method ◽  
Recovery System ◽  
Recovery Performance ◽  
Stiffness And Damping Coefficient ◽  
Stiffness And Damping ◽  
Type Recovery

Vertical rope-type recovery system is a new accurate recovery method for small fixed-wing unmanned aerial vehicles (UAV). This paper models the recovery system and finishes the simulation of recovery process. The recovery stand model of the recovery system is built by analyzing the physical essence, and the aircraft model is based on dynamics and kinematics equations as well as the mechanical characteristics of arresting rope. Finally the simulation analysis of the recovery process is completed. On the basis of the modeling simulation, the system parameters’ effect on recovery performance such as maximum resistance force, maximum overload and maximum radius has been discussed by varying each key parameter. Eventually, the pattern of how initial conditions when hitting the rope as well as the arresting rope’s stiffness and damping coefficient influence recovery performance has been researched, and the result could provide theoretical reference for the design of UAV using vertical rope-type recovery and the system in the future.

scholarly journals Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 436
Author(s):  
Yunfei Cheng ◽  
Ying Hu ◽  
Mengshu Hou ◽  
Tongjie Pan ◽  
Wenwen He ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Compressed Sensing ◽  
Health Monitoring ◽  
Prior Information ◽  
Classification Performance ◽  
Experimental Results ◽  
Measurement Matrix ◽  
Ecg Signals ◽  
Proposed Model ◽  
Atrial Fibrillation Detection

In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing.

Sign in / Sign up

Export Citation Format

Share Document