Optimal Spatial Adaptation for Patch-Based Image Denoising
Citations Over TimeTop 1% of 2006 papers
Abstract
A novel adaptive and patch-based approach is proposed for image denoising and representation. The method is based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. Our contribution is to associate with each pixel the weighted sum of data points within an adaptive neighborhood, in a manner that it balances the accuracy of approximation and the stochastic error, at each spatial position. This method is general and can be applied under the assumption that there exists repetitive patterns in a local neighborhood of a point. By introducing spatial adaptivity, we extend the work earlier described by Buades et al. which can be considered as an extension of bilateral filtering to image patches. Finally, we propose a nearly parameter-free algorithm for image denoising. The method is applied to both artificially corrupted (white Gaussian noise) and real images and the performance is very close to, and in some cases even surpasses, that of the already published denoising methods.
Related Papers
- → Joint bilateral filtering based non-local means image denoising(2014)5 cited
- Patch-Based Bilateral Filter and Local M-Smoother for Image Denoising(2009)
- → K-MEANS BASED IMAGE DENOISING USING BILATERAL FILTERING AND TOTAL VARIATION(2014)
- Similar Pixel Selection Based on Bilateral Filter for Image Denoising(2010)
- A Novel Architecture of Bilateral Filter for Image Denoising(2018)