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Open Access Self-Example-Based Edge Enhancement Algorithm for Around View Monitor Images

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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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Keywords: AVM; EDGE ENHANCEMENT; SELF-EXAMPLE; SUPER-RESOLUTION

Document Type: Research Article

Publication date: January 29, 2017

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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