In this paper we consider an image decomposition model that provides a novel framework for image denoising. The model computes the components of the image to be processed in a moving frame that encodes its local geometry (directions of gradients and level-lines). Then, the strategy we develop is to denoise the components of the image in the moving frame in order to preserve its local geometry, which would have been more affected if processing the image directly. Experiments on a whole image database tested with several denoising methods show that this framework can provide better results than denoising the image directly, both in terms of PSNR and SSIM [27] metrics.

ER -
TY - CONF
T1 - Denoising an Image by Denoising its Components in a Moving Frame
T2 - International Conference on Image and Signal Processing (ICISP). *Best Paper Award*
Y1 - 2014
A1 - Gabriela Ghimpeteanu
A1 - Thomas Batard
A1 - Marcelo Bertalmío
A1 - Stacey Levine
AB - In this paper, we provide a new non-local method for image denoising. The key idea we develop is to denoise the components of the image in a well-chosen moving frame instead of the image itself. We prove the relevance of our approach by showing that the PSNR of a grayscale noisy image is lower than the PSNR of its components. Experiments show that applying the Non Local Means algorithm of Buades et al. [5] on the components provides better results than applying it directly on the image.

JF - International Conference on Image and Signal Processing (ICISP). *Best Paper Award* ER - TY - JOUR T1 - Denoising an Image by Denoising its Curvature Image JF - SIAM Journal on Imaging Sciences (SIIMS) Y1 - 2013 A1 - Marcelo Bertalmío A1 - Stacey Levine AB -In this article we argue that when an image is corrupted by additive noise, its curvature image is less aected by it, i.e. the PSNR of the curvature image is larger. We speculate that, given a denoising method, we may obtain better results by applying it to the curvature image and then reconstructing from it a clean image, rather than denoising the original image directly. Numerical experiments conrm this for several PDE-based and patch-based denoising algorithms.

ER - TY - CONF T1 - "Denoising an image by denoising its curvature image", IMA Preprint Y1 - 2012 A1 - Marcelo Bertalmío A1 - Stacey Levine AB -In this article we show that when an image is corrupted by additive noise, its curvature image is less aected by it, i.e. the PSNR of the curvature image is larger. We conjecture that, given a denoising method, we may obtain better results by applying it to the curvature image and then reconstructing from it a clean image, rather than denoising the original image directly. Numerical experiments conrm this for several PDE-based and patch-based denoising algorithms. The improvements in the quality of the results bring us closer to the optimal bounds recently derived by Levin et al. [1, 2].