WSEAS Transactions on Signal Processing


Print ISSN: 1790-5052
E-ISSN: 2224-3488

Volume 14, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.


High Resolution Image Reconstruction Using Fast Compressed Sensing Based on Iterations

AUTHORS: Muhammad Sameer Sheikh, Qunsheng Cao, Caiyun Wang

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ABSTRACT: As a powerful high resolution image modeling technique, compressive sensing (CS) has been successfully applied in digital image processing and various image applications. This paper proposes a new method of efficient image reconstruction based on the Modified Frame Reconstruction Iterative Thresholding Algorithm (MFR ITA) developed under the compressed sensing (CS) domain by using total variation algorithm. The new framework is consisted of three phases. Firstly, the input images are processed by the multilook processing with their sparse coefficients using the Discrete Wavelet Transform (DWT) method. Secondly, the measurements are obtained from sparse coefficient by using the proposed fusion method to achieve the balance resolution of the pixels. Finally, the fast CS method based on the MFR ITA is proposed to reconstruct the high resolution image. In addition, the proposed method achieves good PNSR and SSIM values, and has shown faster convergence rate when performed the MFR ITA under the CS domain. Furthermore the graphical representation demonstrated that the proposed method achieves better performance in terms of the SNR reconstruction and the probability of successfully recovered signal, and also outperforms several other methods.

KEYWORDS: Compressed Sensing, Image Reconstruction, Multilook, MFRITA, Thresholding

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WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 14, 2018, Art. #7, pp. 50-59


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