Compressive sensing-based image denoising using adaptive multiple samplings and reconstruction error control

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.