Paper
8 June 2012 Compressive sensing-based image denoising using adaptive multiple samplings and reconstruction error control
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Abstract
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wonseok Kang, Eunsung Lee, Sangjin Kim, Doochun Seo, and Joonki Paik "Compressive sensing-based image denoising using adaptive multiple samplings and reconstruction error control", Proc. SPIE 8365, Compressive Sensing, 83650Y (8 June 2012); https://doi.org/10.1117/12.920550
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Cited by 1 scholarly publication.
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KEYWORDS
Remote sensing

Denoising

Image processing

Matrices

Wavelets

Image compression

Compressed sensing

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