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Improved Bayesian Compressive Sensing for Image Reconstruction Using Single-Level Wavelet Transform

机译：改进的贝叶斯压缩感知用于单级小波变换的图像重建

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This paper discusses image reconstruction based on the improved Bayesian compressive sensing using the single-level wavelet transform. By using the single-level wavelet transform, the image is decomposed into low-frequency and high-frequency wavelet coefficients. Then the measurements from the high-frequency wavelet coefficients are used to reconstruct the high-frequency wavelet coefficients. Finally, the image is reconstructed by combining the unchanged low-frequency coefficients and the reconstructed high frequency components. Compared with the basis pursuit (BP) algorithm and orthogonal matching pursuit (OMP) algorithm, the Bayesian compressive sensing (BCS) algorithm can effectively reduce the reconstruction errors and improve the peak signal-to-noise ratio (PSNR) of the reconstructed images with less computational complexity. Moreover, in the Bayesian compressive sensing algorithm, different measurement matrices such as Gaussian random matrix, Bernoulli matrix, Toeplitz matrix, and Hadamard matrix are compared to reconstruct the images.
机译：本文讨论了基于改进的贝叶斯压缩感知的单级小波变换的图像重建。通过使用单级小波变换，图像被分解为低频和高频小波系数。然后，将高频小波系数的测量结果用于重构高频小波系数。最后，通过结合不变的低频系数和重构的高频分量来重构图像。与基本追踪（BP）算法和正交匹配追踪（OMP）算法相比，贝叶斯压缩感知（BCS）算法可以有效地减少重建误差并提高重建图像的峰值信噪比（PSNR）。较少的计算复杂度。此外，在贝叶斯压缩感测算法中，比较了诸如高斯随机矩阵，伯努利矩阵，托普利兹矩阵和哈达玛矩阵之类的不同测量矩阵以重建图像。

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