Compressed Sensing Based on Best Wavelet Packet Basis for Image Processing

Li, Zhetao; Xie, Jibo; Zhou, Yu; Tu, Dengbiao; Choi, Young‐June

doi:10.4304/jcp.8.8.1947-1950

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…There are a lot of methods of image processing [19][20][21]. In this paper, three images of a young woman's body were captured to obtain the human body dimensions: the front image, the side image in the standing position, and the side image in the sitting position.…”

Section: A Image Capturingmentioning

confidence: 99%

Edge Extraction for Human Body Images based on ACAFCM Algorithm

Jing¹,

2014

JSW

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Image edge extraction is an important process in obtaining anthropometric information from human body images captured by ordinary cameras. Depending on the characteristics of the large pixel matrix and the fuzzy edge of the image, the ant colony and fuzzy C-means clustering (ACAFCM) algorithm is used to get a clear and complete edge contour of the human body. This Canny algorithm is used to preprocess image segmentation, binarization, and de-noising by simplifying the image color and noise. The ant colony algorithm determines the initial center and number of clusters, and it distinguishes the pixels, based on the target, background, edge, and noise of the pretreated image. The FCM algorithm clusters the pixels with the same attributes into one class, and it extracts a clear and complete edge contour of the body image.

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Section: A Image Capturingmentioning

confidence: 99%

Edge Extraction for Human Body Images based on ACAFCM Algorithm

Jing¹,

2014

JSW

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“…In many applications such as statistical regression [1], digital communications [2], image processing [3, 4], multimedia sensor networks [5, 6], interpolation/extrapolation [7], and signal deconvolution [8, 9], recovering high-dimensional signals from relatively fewer measurements is a challenging task. Fortunately, in the real world many signals are, or can be, transformed (such as DCT, wavelet packet transform [10]) to sparse such that only a small part of signal coefficients are nonzero values. And compressed sensing [11, 12] allows us to recover sparse signal from high-dimensional signals with very few measurements.…”

Section: Introductionmentioning

confidence: 99%

Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing

Xie

et al. 2013

International Journal of Distributed Sensor Networks

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In this paper, an algorithm named stepwise subspace pursuit (SSP) is proposed for sparse signal recovery. Unlike existing algorithms that select support set from candidate sets directly, our approach eliminates useless information from the candidate through threshold processing at first and then recovers the signal through the largest correlation coefficients. We demonstrate that SSP significantly outperforms conventional techniques in recovering sparse signals whose nonzero values have exponentially decaying magnitudes or distribution of (0, 1). Experimental results of Lena show that SSP is better than CoSaMP, OMP, and SP in terms of peak signal to noise ratio (PSNR) by 5.5 db, 4.1 db, and 4.2 db, respectively.

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Compressed Sensing Based on Best Wavelet Packet Basis for Image Processing

Cited by 2 publications

References 12 publications

Edge Extraction for Human Body Images based on ACAFCM Algorithm

Edge Extraction for Human Body Images based on ACAFCM Algorithm

Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing

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