Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution

Hang Yang, Ming Zhu, Yan Niu, Yujing Guan, Zhongbo Zhang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 705-713

Abstract


Image deconvolution continues to be an active research topic of recovering a sharp image, given a blurry one generated by a convolution. One of the most challenging problems in image deconvolution is how to preserve the fine scale texture structures while removing blur and noise. Various methods have been implemented in both spatial and transform domains, such as gradient based methods, nonlocal self-similarity methods, sparsity based methods. However, each domain has its advantages and shortcomings, which can be complemented by each other. In this work we propose a new approach for efficient image deconvolution based on dual domain filters. In the deblurring process, we offer a hybrid method that a novel rolling guidance filter is used to ensure proper texture/structure separation, and then in the transform domain, we use the short-time Fourier transform to recover the textures while removing noise with energy shrinkage. Our hybrid algorithm that is surprisingly easy to implement, and experimental results clearly show that the proposed algorithm outperforms many state-of-the-art deconvolution algorithms in terms of both quantitative measure and visual perception quality.

Related Material


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[bibtex]
@InProceedings{Yang_2015_CVPR,
author = {Yang, Hang and Zhu, Ming and Niu, Yan and Guan, Yujing and Zhang, Zhongbo},
title = {Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}