Blur Processing Using Double Discrete Wavelet Transform

Yi Zhang, Keigo Hirakawa; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1091-1098

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Zhang_2013_CVPR,
author = {Zhang, Yi and Hirakawa, Keigo},
title = {Blur Processing Using Double Discrete Wavelet Transform},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}