Deblurring Text Images via L0-Regularized Intensity and Gradient Prior


Jinshan Pan     Zhe Hu§      Zhixun Su      Ming-Hsuan Yang§

DLUT        §UC Merced

IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014)

Abstract

We propose a simple yet effective L0-regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is motivated by observing distinct properties of text images. Based on this prior, we develop an efficient optimization method to generate reliable intermediate results for kernel estimation. The proposed method does not require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring algorithms. We discuss the relationship with other deblurring algorithms based on edge selection and provide insight on how to select salient edges in a more principled way. In the final latent image restoration step, we develop a simple method to remove artifacts and render better deblurred images. Experimental results demonstrate that the proposed algorithm performs favorably against the stateof- the-art text image deblurring methods. In addition, we show that the proposed method can be effectively applied to deblur low-illumination images.

Paper and MATLAB code

The paper and MATLAB code can be found here. [PDF] [Poster] [Blind deconvolution code] [Non-blind deconvolution code]

BibTex

@inproceedings{hu_cvpr2014_textdeblur,
title = {Deblurring Text Images via L0-Regularized Intensity and Gradient Prior },
author = {Jinshan Pan, Zhe Hu, Zhixun Su and Ming-Hsuan Yang},
journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014)},
year = {2014}
}