Recovering wavelet relations using SVM for image denoising

Here we propose an alternative non-explicit way to take into account the relations among wavelet coefficients in natural images for denoising: we use Support Vector Machines (SVM) to learn these relations. Since relations among the coefficients are specific to the signal, SVM regularization removes the noise, which does not share this property. Moreover, due to its non-parametric nature, the method can eventually cope with different noise sources. The results show that: (1) the proposed non-parametric method outperforms conventional methods that assume coefficient independence, and (2) its performance is similar to state-of-the-art parametric methods that do explicitly include these relations. Therefore, the proposed machine learning approach can be seen as a more flexible (model-free) alternative to the explicit description of wavelet coefficient relations in Bayesian approaches.

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Valero Laparra, Juan Gutierrez, Gustavo Camps-Valls, Jesus Malo
• Keywords: Alternative Non-explicit Way, ICIP 2008, Image Processing, Non-parametric Method Outperforms, SVM Regularization, Wavelet Coefficient Relations, Wavelet Coefficients