MRA-based wavelet frames and applications: image segmentation and surface reconstruction

Bin Dong; Zuowei Shen

doi:10.1117/12.923203

3 May 2012 MRA-based wavelet frames and applications: image segmentation and surface reconstruction

Bin Dong, Zuowei Shen

Proceedings Volume 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X; 840102 (2012) https://doi.org/10.1117/12.923203
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Theory of wavelet frames and their applications to image restoration problems have been extensively studied for the past two decades. The success of wavelet frames in solving image restoration problems, which includes denoising, deblurring, inpainting, computed tomography, etc., is mainly due to their capability of sparsely approximating piecewise smooth functions such as images. However, in contrast to the wide applications of wavelet frame based approaches to image restoration problems, they are rarely used for some image/data analysis tasks, such as image segmentation, registration and surface reconstruction from unorganized point clouds. The main reason for this is the lack of geometric interpretations of wavelet frames and their associated transforms. Recently, geometric meanings of wavelet frames have been discovered and connections between the wavelet frame based approach and the differential operator based variational model were established.1 Such discovery enabled us to extend the wavelet frame based approach to some image/data analysis tasks that have not yet been studied before. In this paper, we will provide a unified survey of the wavelet frame based models for image segmentation and surface reconstruction from unorganized point clouds. Advantages of the wavelet frame based approach are illustrated by numerical experiments.

Citation Download Citation

Bin Dong and Zuowei Shen "MRA-based wavelet frames and applications: image segmentation and surface reconstruction", Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 840102 (3 May 2012); https://doi.org/10.1117/12.923203

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