2007 IEEE International Conference on Image Processing - San Antonio, Texas, U.S.A. - September 16-19, 2007

Technical Program

Paper Detail

Paper:TP-P5.12
Session:Image and Video Artifact Removal and Denoising
Time:Tuesday, September 18, 14:30 - 17:10
Presentation: Poster
Title: IMAGE DENOISING THROUGH SUPPORT VECTOR REGRESSION
Authors: Dalong Li; Hewlett Packard Laboratories 
 Steven Simske; Hewlett Packard Laboratories 
 Russell Mersereau; Georgia Institute of Technology 
Abstract: In this paper, an example-based image denoising algorithm is introduced. Image denoising is formulated as a regression problem, which is then solved using support vector regression (SVR). Using noisy images as training sets, SVR models are developed. The models can then be used to denoise different images corrupted by random noise at different levels. Initial experiments show that SVR can achieve a higher peak signal-to-noise ratio (PSNR) than the multiple wavelet domain Besov ball projection method on document images.



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