{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T01:15:52Z","timestamp":1676423752473},"reference-count":24,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2012,3]]},"abstract":"<jats:p> In this paper, we first present an adaptive intra-scale noise removal scheme, and estimate clean wavelet coefficients using new prior information with Bayesian estimation techniques. A new model using the non-informative improper Jeffreys' prior is given under the supposed Gaussian distribution for orthogonal wavelet transformation. Then, we propose a computationally feasible adaptive noise smoothing algorithm that considers the dependency characteristics of images. The wavelet coefficients are assumed to be non-Gaussian random variables for non-orthogonal redundancy transformation. The variances of the wavelet coefficients are estimated locally by a centered square-shaped window for every pixel within each subband. The experimental results show that the orthogonal wavelet transformation provides better results at the Gaussian assumption, while the non-orthogonal redundancy wavelet transformation performance tends to increase when the non-Gaussian bivariate distribution is used. <\/jats:p>","DOI":"10.1142\/s0219691312500142","type":"journal-article","created":{"date-parts":[[2012,2,15]],"date-time":"2012-02-15T02:25:24Z","timestamp":1329272724000},"page":"1250014","source":"Crossref","is-referenced-by-count":4,"title":["IMAGE DENOISING BASED ON GAUSSIAN AND NON-GAUSSIAN ASSUMPTION"],"prefix":"10.1142","volume":"10","author":[{"given":"PING","family":"ZHAO","sequence":"first","affiliation":[{"name":"School of Science, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZHAOWEI","family":"SHANG","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"CHUN","family":"ZHAO","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics Science, Tianjin Normal University, Tianjin 300074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2012,5]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2004.839692"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr:20070035"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2007.0096"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1109\/83.862630"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1109\/83.862633"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1109\/78.668544"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/81.3.425"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1109\/83.941856"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.1999.0447"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1006\/acha.2000.0343"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1142\/S021969131000350X"},{"key":"rf12","first-page":"300","volume":"3","author":"Li X.","journal-title":"Proc. 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