کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
526952 | 869265 | 2009 | 5 صفحه PDF | دانلود رایگان |
Support vector regression (SVR) has been applied for blind image deconvolution. In this correspondence, it is applied in the problem of image denoising. After training on noisy images with ground-truth, support vectors (SVs) are identified and their weights are computed. Then the SVs and their weights are used in denoising different images corrupted by random noise at different levels on a pixel-by-pixel basis. The proposed SVR based image denoising algorithm is an example-based approach since it uses SVs in denoising. The SVR denoising is compared with a multiple wavelet domain method (Besov ball projection). Some initial experiments indicate that SVR based image denoising outperforms Besov ball projection method on non-natural images (e.g. document images) in terms of both peak signal-to-noise ratio (PSNR) and visual inspection.
Journal: Image and Vision Computing - Volume 27, Issue 6, 4 May 2009, Pages 623–627