Image Denoising Through Support Vector Regression
- 1 January 2007
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2007 IEEE International Conference on Image Processing
- Vol. 4, IV - 425-IV - 428
- https://doi.org/10.1109/icip.2007.4380045
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.Keywords
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