Recovering Wavelet Relations Using SVM for Image Denoising

View Researcher II's Other Codes

Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”

Please contact us in case of a broken link from here

Authors V. Laparra, J. GutiƩrrez, G. Camps and J. Malo
Journal/Conference Name IEEE International Conference on Image Processing (ICIP)
Paper Category
Paper Abstract 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.
Date of publication 2008
Code Programming Language MATLAB
Comment

Copyright Researcher II 2021