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Volume: 29 | Article ID: art00012
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Self-Example-Based Edge Enhancement Algorithm for Around View Monitor Images
  DOI :  10.2352/ISSN.2470-1173.2017.2.VIPC-408  Published OnlineJanuary 2017
Abstract

In general, edges in the peripheral areas of around view monitor (AVM) wide-angle (WA) images tend to be blurred. This paper proposes a self-example-based edge enhancement algorithm to improve the definition of such edges. First, a low-resolution (LR) version of a blurred WA high-resolution (HR) image is produced via down-scaling. Next, a proper self-example for each non-overlapped patch in the HR image is found within the LR image in terms of selfsimilarity. Then, high frequency information is extracted from the found LR patch, and it is finally added to the input HR patch. Experimental results show that the proposed algorithm provides higher JNBM values than previous works with outstanding visual quality.

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Dong Yoon Choi, Ji Hoon Choi, Jin Wook Choi, Byung Cheol Song, "Self-Example-Based Edge Enhancement Algorithm for Around View Monitor Imagesin Proc. IS&T Int’l. Symp. on Electronic Imaging: Visual Information Processing and Communication VIII,  2017,  pp 60 - 64,  https://doi.org/10.2352/ISSN.2470-1173.2017.2.VIPC-408

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Copyright © Society for Imaging Science and Technology 2017
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Electronic Imaging
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