@inproceedings{ba04f67e8c6c4e059e43833b67889668,
title = "Novel information processing for image de-noising based on sparse basis",
abstract = "Image de-noising is one of the important information processing technologies and a fundamental image processing step for improving the overall quality of medical images. Conventional de-noising methods, however, tend to over-suppress high-frequency details. To overcome this problem, in this paper we present a novel compressive sensing (CS) based noise removing algorithm using proposed sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the transform coefficients of the noisy image for compressive sampling. The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct image from noisy sparse image. In the reconstruction process, the proposed threshold with Bayeshrink thresholding strategies is used. Experimental results demonstrate that the proposed method removes noise much better than existing state-of-the-art methods in the sense image quality valuation indexes.",
keywords = "ATVD, BP, CS, OMP, Sparse",
author = "{Rabiul Islam}, {Sheikh Md} and Xu Huang and Keng-Liang Ou and Rojas, {Raul Fernandez} and Hongyan Cui",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
doi = "10.1007/978-3-319-26555-1_50",
language = "English",
isbn = "9783319265544",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "443--451",
editor = "Tingwen Huang and Qingshan Liu and Lai, {Weng Kin} and Sabri Arik",
booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",
}