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Proceedings Paper

Improved Edge Directed Super-Resolution (EDSR) with hardware realization for surveillance, transportation, and multimedia applications
Author(s): Yue Wang; Osborn de Lima; Eli Saber; Kurt Robert Bengtson
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Paper Abstract

In this paper, we present an improved Edge Directed Super Resolution (EDSR) technique to produce enhanced edge definition and improved image quality in the resulting high resolution image. The basic premise behind this algorithm remains, like its predecessor, to utilize gradient and spatial information and interpolate along the edge direction in a multiple pass iterative fashion. The edge direction map generated from horizontal and vertical gradients and resized to the target resolution is quantized into eight directions over a 5 × 5 block compared to four directions over a 3 × 3 block in the previous algorithm. This helps reduce the noise caused in part due to the quantization error and the super resolved results are significantly improved. In addition, an appropriate weighting encompassing the degree of similarity between the quantized edge direction and the actual edge direction is also introduced. In an attempt to determine the optimal super resolution parameters for the case of still image capture, a hardware setup was utilized to investigate and evaluate those factors. In particular, the number of images captured as well as the amount of sub pixel displacement that yield a high quality result was studied. This is done by utilizing a XY stage capable of sub-pixel movement. Finally, an edge preserving smoothing algorithm contributes to improved results by reducing the high frequency noise introduced by the super resolution process. The algorithm showed favorable results on a wide variety of datasets obtained from transportation to multimedia based print/scan application in addition to images captured with the aforementioned hardware setup.

Paper Details

Date Published: 5 March 2014
PDF: 12 pages
Proc. SPIE 9026, Video Surveillance and Transportation Imaging Applications 2014, 902603 (5 March 2014); doi: 10.1117/12.2042696
Show Author Affiliations
Yue Wang, Rochester Institute of Technology (United States)
Osborn de Lima, Rochester Institute of Technology (United States)
Eli Saber, Rochester Institute of Technology (United States)
Kurt Robert Bengtson, Hewlett-Packard Co. (United States)

Published in SPIE Proceedings Vol. 9026:
Video Surveillance and Transportation Imaging Applications 2014
Robert P. Loce; Eli Saber; Ned Lecky, Editor(s)

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