Sparse Multiscale Patches for Image Processing

Abstract. This paper presents a framework to define an objective measure of the similarity (or dissimilarity) between two images for image processing. The problem is twofold: 1) define a set of features that capture the information contained in the image relevant for the given task and 2) define a similarity measure in this feature space. In this paper, we propose a feature space as well as a statistical measure on this space. Our feature space is based on a global descriptor of the image in a multiscale transformed domain. After decomposition into a Laplacian pyramid, the coefficients are arranged in intrascale/interscale/interchannel patches which reflect the dependencies between neighboring coefficients in presence of specific structures or textures. At each scale, the probability density function (pdf) of these patches is used as a descriptor of the relevant information. Because of the sparsity of the multiscale transform, the most significant patches, called Sparse Multiscale Patc...

• Type: Conference
• Year: 2008
• Where: ETVC
• Authors: Paolo Piro, Sandrine Anthoine, Eric Debreuve, Michel Barlaud
• Keywords: Applied Computing, ETVC 2008, Feature Space, K-th Nearest Neighbor, Probability Density Functions