NDLI: Document image super-resolution using structural similarity and Markov random field

Content Provider	IET Digital Library
Author	Chen, Xiaoxuan Qi, Chun
Abstract	Low-resolution (LR) document images may cause difficulties in reading or low recognition rates in computer vision. Thus, it is necessary to improve the resolution of an LR document image via some algorithms. In this study, a novel document image super-resolution (SR) method using structural similarity and Markov random field (MRF) is proposed. First, the non-local algorithm is utilised to find similar patches. Instead of using the Euclidian distance, a modified chi-square distance is proposed to measure the patch similarity because the bimodality characteristic of the document images can be better described by this modified chi-square distance. Finally, the structural similarity of similar patches is served as a constraint for the MRF-based SR method, which is proper to describe the neighbouring relationship between patches. The SR reconstruction for LR images of printed and handwritten documents are carried out by the proposed algorithm. Experimental results show that the reconstructed SR images obtain higher peak signal-to-noise ratio and structural similarity values than those of several state-of-the-art SR methods and visually pleasant SR images can be produced as well.
Starting Page	687
Ending Page	698
Page Count	12
ISSN	17519659
Volume Number	8
e-ISSN	17519667
Issue Number	Issue 12, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/8/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2013.0412
Journal	IET Image Processing
Publisher Date	2014-05-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision Computer Vision And Image Processing Technique Document Image Bimodality Characteristic Document Image Processing Document Image SR Method Document Image Super-resolution Method Document Processing Technique Euclidian Distance Handwritten Document Image Reconstruction Image Resolution Low-resolution Document Image LR Document Image Markov Processes Markov Random Field Modified Chi-square Distance MRF-based SR Method Neighbouring Relationship Nonlocal Algorithm Optical, Image And Video Signal Processing Patch Similarity Peak Signal-to-noise Ratio Printed Document Recognition Rate Reconstructed SR Image Resolution Improvement Structural Similarity Value
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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