A Content-Adaptive Sharpness Enhancement Algorithm using 2D FIR Filters Trained by Pre-Emphasis

Title
A Content-Adaptive Sharpness Enhancement Algorithm using 2D FIR Filters Trained by Pre-Emphasis
Authors
송병철
Keywords
Pre-emphasis Sharpening Learning FIR Dictionary Content-adaptive Synthesis Patch
Issue Date
2013
Publisher
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
Series/Report no.
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION ; Vol24 no.5 Startpage 579 Endpage 591
Abstract
This paper proposes a content-adaptive sharpening algorithm using two-dimensional (2D) FIR filters trained by pre-emphasis for various image pairs. In the learning stage, all low-quality (LQ) and high-quality (HQ) image pairs are first pre-emphasized, i.e., properly sharpened. Then selective 2D FIR filter coef- ficients for high-frequency synthesis are trained using the pre-emphasized LQ?HQ image pairs, and then are stored in a dictionary that resembles an LUT (look-up table). In the inference stage, each input image is pre-emphasized in the same manner as in the learning stage. The best-matched 2D filter for each LQ patch is then found in the dictionary, and an HQ patch corresponding to the input LQ patch is synthesized using the resultant 2D FIR filter. The experiment results show that the proposed algorithm visually outperforms existing ones and that the mean of absolute errors (MAEs) and MSSSIM (multi-scale structure similarity) of the proposed algorithm are about 10% to 60% lower and about 0.002?0.053 higher, respectively than those of the existing algorithms.
URI
http://dx.doi.org/10.1016/j.jvcir.2013.04.003
http://dspace.inha.ac.kr/handle/10505/33414
ISSN
1047-3203
Appears in Collections:
College of Engineering(공과대학) > Electronic Engineering (전자공학) > Journal Papers, Reports(전자공학 논문, 보고서)
Files in This Item:
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