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High Resolution Image Reconstruction using Fast Compressed Sensing based on Iterations

机译：基于迭代的快速压缩检测高分辨率图像重建

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摘要

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.
机译：作为一种强大的高分辨率图像建模技术，已成功应用于数字图像处理和各种图像应用的压缩感测（CS）。本文提出了一种基于通过使用总变化算法在压缩感测（CS）域下方开发的修改帧重建迭代阈值（MFR ITA）的高效图像重建方法。新框架由三个阶段组成。首先，通过使用离散小波变换（DWT）方法，通过具有它们的稀疏系数的多圆处理来处理输入图像。其次，通过使用所提出的融合方法实现稀疏系数来获得测量来实现像素的平衡分辨率。最后，提出了基于MFR ITA的快速CS方法来重建高分辨率图像。此外，所提出的方法实现了良好的PNSR和SSIM值，并且在CS域下执行MFR ITA时，已经提高了更快的收敛速率。此外，图形表示表明，所提出的方法在SNR重建方面实现了更好的性能，以及成功恢复信号的概率，并且还优于其他几种方法。

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