Image Denoising with Kernels Based on Natural Image Relations

A successful class of image denoising methods is based on Bayesian approaches working in wavelet representations. The performance of these methods improves when relations among the local frequency coefficients are explicitly included. However, in these techniques, analytical estimates can be obtained only for particular combinations of analytical models of signal and noise, thus precluding its straightforward extension to deal with other arbitrary noise sources. In this paper, we propose an alternative non-explicit way to take into account the relations among natural image wavelet coefficients for denoising: we use support vector regression (SVR) in the wavelet domain to enforce these relations in the estimated signal. Since relations among the coefficients are specific to the signal, the regularization property of SVR is exploited to remove the noise, which does not share this feature. The specific signal relations are encoded in an anisotropic kernel obtained from mutual information...

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Valero Laparra, Juan Gutierrez, Gustavo Camps-Valls, Jesús Malo
• Keywords: Arbitrary Noise Sources, JMLR 2010, Noise Sources, Wavelet