An Image Zooming Technique Based on Vector Quantization Approximation

[[abstract]]An image zooming method based on vector quantization approximation for magnifying gray-scale and color image by a factor of 2 is proposed. In our proposed method, the unknown pixel values on the image are interpolated by using a vector quantization codebook based on their local information. In comparison of our method with the locally adaptive zooming algorithm published in [S. Battiato, G. Gallo, F. Stanco, A locally adaptive zooming algorithm for digital images, Image and Vision Computing, 20(11) (2002) 805–812.], our experimental results have demonstrated that the image quality of the enlarged image is superior to the method in [S. Battiato, G. Gallo, F. Stanco, A locally adaptive zooming algorithm for digital images, Image and Vision Computing, 20(11) (2002) 805–812.]. Not only is our method simpler to implement by utilizing a table look-up technique on codebook, but also is much easier in translating to color images than that of [S. Battiato, G. Gallo, F. Stanco, A locally adaptive zooming algorithm for digital images, Image and Vision Computing, 20(11) (2002) 805–812.] by replacing an adequate codebook

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Model Selection for Geometric Fitting: Geometric Ale and Geometric MDL

Contrasting "geometric fitting", for which the noise level is taken as the asymptotic variable, with "statistical inference", for which the number of observations is taken as the asymptotic variable, we give a new definition of the "geometric AIC" and the "geometric MDL" as the counterparts of Akaike's AIC and Rissanen's MDL. We discuss various theoretical and practical problems that emerge from our analysis. Finally, we show, doing experiments using synthetic and real images, that the geometric MDL does not necessarily outperform the geometric AIC and that the two criteria have very different characteristics

New adaptive pixel decimation for block motion vector estimation

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