Image denoising is a fundamental image processing step for improving the overall quality of images. It is more
important for remote sensing images because they require significantly higher visual quality than others. Conventional
denoising methods, however, tend to over-suppress high-frequency details. To overcome this problem, we present a
novel compressive sensing (CS)-based noise removing algorithm using adaptive multiple samplings and reconstruction
error control. We first decompose an input noisy image into flat and edge regions, and then generate 8x8 block-based
measurement matrices with Gaussian probability distributions. The measurement matrix is applied to the first three
levels of wavelet transform coefficients of the input image for compressive sampling. The orthogonal matching pursuit
(OMP) is applied to reconstruct each block. In the reconstruction process, we use different error threshold values
according to both the decomposed region and the level of the wavelet transform based on the fast that the first level
wavelet coefficients in the edge region have the lowest error threshold, whereas the third level wavelet coefficients in
the flat region have the highest error threshold. By applying adaptive threshold value, we can reconstruct the image
without noise. Experimental results demonstrate that the proposed method removes noise better than existing state-ofthe-
art methods in the sense of both objective (PSNR/MSSIM) and subjective measures. We also implement the
proposed denoising algorithm for remote sensing images with by minimizing the computational load.
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