Image Denoising and Magnification via Kernel Fitting and Modified SVD

摘要：

Image denoising and magnification play an important role in most visual applications such as visual material examination for public security and image-based medical diagnosis. We propose a 1-D kernel fitting algorithm for denoising in space domain and wavelet transformed (WT) domain, and for magnification in space domain. In the algorithm, the values of a column or a row from an image or its transformed version are taken as the measured results of a fitting function. The fitting coefficients are estimated by least square (LS) method. An image is denoised or magnified by resampling the fitted function, or followed by inverse transform if fitting is carried out in a transformed domain. We also discuss a modified singular value decomposition (SVD) method for comparison. To illustrate the application feasibility, the presented methods are experimentally compared with the basic wavelet-thresholding algorithm for image denoising, and with the standard bicubic interpolation method for magnification.

关键词： image denoising image magnification kernel fitting modified SVD estimate

作者: Benyong Liu Xiang Liao

作者单位: Department of Computer Science Guizhou University Guiyang,550025,China Key Lab of Audio-Visual Material Examination Guizhou Public Security Department Guiyang,550001,China

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 521-524

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）