Improved Measure Algorithm Based On Cosamp For Image Recovery

Wu, Guangyuan; Li, Xingkun; Dai, Jiyang

doi:10.21307/ijssis-2017-678

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Many scholars have put forward corresponding improvement schemes to solve the problems aiming at high accuracy, stability and efficiency. Reference [9] presented the improved measurement algorithm based on cosamp for image recovery. Reference [10] presented detecting wormhole attacks in Wireless Sensor Networks using hop count analysis.…”

Section: Introductionmentioning

confidence: 99%

Iterative L-M Algorithm in WSN – Utilizing Modifying Average Hopping Distances

2017

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Abstract-Wireless Sensor Networks (WSN) are getting more and more attention for various applications. In addition, localization plays an important role in WSN. This article focus on the large errors of DV-Hop (Distance VectorHop) localization algorithm in net topology of randomly distributed notes. A CLDV-Hop (Correct Levenberg Marquardt DV-Hop) algorithm in DV-Hop based on modifying average hopping distances is proposed. Firstly, hop count threshold is to optimize the anchor node during a data exchange. The weighted average hop distance of unknown nodes nearest the three anchor nodes are selected as its average hop distance according to the minimum mean square criteria. Finally, L-M (Levenberg Marquardt) algorithm is used to optimize the coordinate of unknown node estimated by least squares. The simulation results show that, under the conditions of existing overhead and for a given simulation environment, CLDV-Hop algorithm has higher positioning accuracy than existing improved algorithms. The accuracy is improved 33%-41% compared with DV-Hop algorithm. Keywords-wireless sensor networks, DV-Hop algorithm, localization, L-M algorithm IntroductionWireless Sensor Network (WSN) consists of a large number of sensor nodes distributed in the area with perceptive, processing and transmitting environment information. It is often applied in environmental monitoring, medical service, military investigation, industrial diagnosis and intelligent space [1]. Whatever the field is, researches lack of objectives and sensor nodes localization are meaningless. Therefore, node localization technology has become an important research topic in WSN [2].At present, Global Positioning System (GPS) can achieve the node localization of WSN. However, taken price, volume, energy consumption and environment into account, it is impossible to use GPS massively [3]. Many researchers have proposed 4http://www.i-joe.orgPaper-Iterative L-M Algorithm in WSN-Utilizing Modifying Average Hopping Distances various positioning algorithms, which can be divided into range-based localization algorithms (Range-Based) and range-free localization algorithm (Range-Free) [4]. The former requires measuring absolute distance and angle between neighboring nodes. The latter can only implement localization, based on the connectivity of the network, such as centroid algorithm [3][4][5], convex programming algorithm [6], DVHop algorithm [7] and Approximate Point-in-triangulation Test (APIT) algorithm [8].Range-Based has a higher requirement to hardware and it needs repeated measurements to obtain reasonable positioning accuracy. Therefore, it will cost a large amount of computation and communication. Range-Free positioning mechanism has been used widely in large-scale WSN with its economization, power-saving and simplicity. DV-Hop is a typical Range-Free localization algorithm with simple implementation and high positioning accuracy. However, the positioning accuracy of the proposed algorithm is still inadequate in the random distribution of nodes in the WSN. Many sc...

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Section: Introductionmentioning

confidence: 99%

Iterative L-M Algorithm in WSN – Utilizing Modifying Average Hopping Distances

2017

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“…The fusion process is divided into several stages.In the first stage ,the source images are decomposed by NSCT, then we obtain the coefficient matrice of all directional subbands at each of high frequency levels. Here, each of coefficient matrice represents its directional information.Considering the source images may Zhang Pai, IMAGE FUSION BASED ON JOINT NONSUBSAMPLED CONTOURLET AND OVERCOMPLETE BRUSHLET TRANSFORMS include lots of details, the coefficient matrice in the NSCT domain also embody complex directions and textures information [11][12]. In order to highlight salient features of each directional subband,OCBT is employed to decompose all the directional subbands at each high frequency level in NSCT domain.Then the obtained coefficient matrice here can fully represent the feature information of all possible directions, frequencies, and locations.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion Based on Joint Nonsubsampled Contourlet and Overcomplete Brushlet Transforms

Zhang

2016

International Journal on Smart Sensing and Intelligent Systems

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“…To overcome the above problems, by introducing the idea of single image super resolution [6], we designed a multi-scale compressed sensing in Shearlet-domainbased image super resolution fast reconstruction method (MCSS_SR). SR is image processing technology which can be described as to recover a high resolution (HR) image from one or more low resolution (LR) images [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Compressed Sensing Based On Split Augmented Lagrangian Shrinkage Algorithm For Image Super-Resolution Reconstruction

Tian

2016

International Journal on Smart Sensing and Intelligent Systems

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This paper proposes a multi-scale compressed sensing algorithm in shearlet domain by exploiting discrete Shearlet transform which can optimally represent the images with edges. An image is decomposed by shearlet. The compressed sensing is deployed in each of the directional sub-bands of high frequency scales. And inverse measurement matrix is modified by the upsampling operator to enhance the resolution of image. In addition, the original image reconstructed by compressed sensing based on super-resolution can produce the low subbands with high frequency. And an split augmented lagrangian shrinkage algorithm is exploited for compressed sensing image reconstruction , which can improve reconstruction image quality and convergencerate. Experimental results has shown that the proposed method can reconstruct the image with less iterative time by using fewer samples, improve the computational efficiency as well as achieve the images with high quality.

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Improved Measure Algorithm Based On Cosamp For Image Recovery

Cited by 6 publications

References 17 publications

Iterative L-M Algorithm in WSN – Utilizing Modifying Average Hopping Distances

Iterative L-M Algorithm in WSN – Utilizing Modifying Average Hopping Distances

Image Fusion Based on Joint Nonsubsampled Contourlet and Overcomplete Brushlet Transforms

Multi-Scale Compressed Sensing Based On Split Augmented Lagrangian Shrinkage Algorithm For Image Super-Resolution Reconstruction

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