Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing

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

doi:10.1155/2013/798537

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…Although SSD greatly enhances the real-time performance, detection for small objects is not effective. Actually, the average length ratio between FODs and whole images is about 0.04, which is usually considered as small target detection problem [ 46 , 47 , 48 , 49 ]. It means that convolution features could possibly be lost through pooling operation with no fully-connect layers.…”

Section: Methodsmentioning

confidence: 99%

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

Cao

Peng

Cai

et al. 2018

Sensors

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In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment.

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Section: Methodsmentioning

confidence: 99%

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

Cao

Peng

Cai

et al. 2018

Sensors

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“…this is a convex optimization problem. In addition, there are many reconstruction algorithms proposed to solve the [39], Basis Pursuit (BP) [38], Orthogonal Matching Pursuit (OMP) [40] and Stepwise Subspace Pursuit (SSP) [41]. In this paper, SSP is adopted to solve the problem (5).…”

Section: Cs In Spatial Domainmentioning

confidence: 99%

Image Arbitrary-Ratio Down- and Up-Sampling Scheme Exploiting DCT Low Frequency Components and Sparsity in High Frequency Components

Zhang

Chen

Shi

et al. 2016

IEICE Trans. Inf. & Syst.

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The development of image acquisition technology and display technology provide the base for popularization of high-resolution images. On the other hand, the available bandwidth is not always enough to data stream such high-resolution images. Down-and up-sampling, which decreases the data volume of images and increases back to high-resolution images, is a solution for the transmission of high-resolution images. In this paper, motivated by the observation that the high-frequency DCT components are sparse in the spatial domain, we propose a scheme combined with Discrete Cosine Transform (DCT) and Compressed Sensing (CS) to achieve arbitrary-ratio down-sampling. Our proposed scheme makes use of two properties: First, the energy of a image concentrates on the lowfrequency DCT components. Second, the high-frequency DCT components are sparse in the spatial domain. The scheme is able to preserve the most information and avoid absolutely blindly estimating the highfrequency components. Experimental results show that the proposed downand up-sampling scheme produces better performance compared with some state-of-the-art schemes in terms of peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM) and processing time.

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“…histogram equalization, curvelet transform, homomorphic filtering, wavelet transform, compression sensing [27], [28] and Retinex methods [29], [30], which can increase the overall contrast, make the brightness of the image suitable, and achieve a satisfactory visual effect. However, sometimes the enhanced image seems unreal.…”

Section: Introductionmentioning

confidence: 99%

Image Haze Removal Based on Superpixels and Markov Random Field

Tan¹,

Guo-yu²

2020

IEEE Access

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Image haze removal is critical for autonomous driving. However, it is a challenging task for the existing image dehazing algorithms to eliminate the block effect completely and handle objects similar to light (such as snowy objects and white buildings). To address this problem, we propose a novel singleimage dehazing method based on superpixels and Markov random field. We obtain the transmission map in the superpixel domain to eliminate the block/halo effect and introduce Markov random field to revise the transmission map in the superpixel domain. The key idea is that the sparsely distributed, incorrectly estimated transmittances can be corrected by properly characterizing the spatial dependencies between the incorrectly estimated superpixels and the neighbouring well-estimated superpixels. The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods. INDEX TERMS Superpixel, Markov random field, haze removal, edge preservation, dark channel prior.

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Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing

Cited by 12 publications

References 22 publications

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

Image Arbitrary-Ratio Down- and Up-Sampling Scheme Exploiting DCT Low Frequency Components and Sparsity in High Frequency Components

Image Haze Removal Based on Superpixels and Markov Random Field

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