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Description

We propose a notion of double discrete wavelet transform (DDWT) that is designed to sparsify the blurred image and the blur kernel simultaneously. DDWT greatly enhances our ability to analyze, detect, and process blur kernels and blurry images—the proposed framework handles both global and spatially varying blur kernels seamlessly, and unifies the treatment of blur caused by object motion, optical defocus, and camera shake. To illustrate the potential of DDWT in computer vision and image processing, we develop example applications in blur kernel estimation, deblurring, and near-blur-invariant image feature extraction.

Publication Date

4-9-2014

Project Designation

Graduate Research

Primary Advisor

Kiego Hirakawa

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium poster

Disciplines

Arts and Humanities | Business | Education | Engineering | Life Sciences | Medicine and Health Sciences | Physical Sciences and Mathematics | Social and Behavioral Sciences

Blur Processing Using Double Discrete Wavelet Transform

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