An optimal region of interest localization using edge refinement filter and entropy-based measurement for point spread function stimation
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Date
2019-10-01
Authors
Ahmad Husni Mohd Shapri
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Abstract
The use of edges to determine an optimal region of interest (ROI) location is
increasingly becoming popular for image deblurring. Recent studies have shown that
regions with strong edges tend to produce better deblurring results. In this study, a
direct method for ROI localization based on edge refinement filter and entropy-based
measurement is proposed. Using this method, the randomness of grey level distribution
is quantitatively measured, from which the ROI is determined. This method has low
computation cost since it contains no matrix operations. The proposed method has
been tested using three sets of test images - Dataset I, II and III. Empirical results
suggest that the improved edge refinement filter is competitive when compared to the
established edge detection schemes and achieves better performance in the Pratt's
figure-of-merit (PFoM) and the twofold consensus ground truth (TCGT); averaging at
15.7 % and 28.7 %, respectively. The novelty of the proposed approach lies in the use
of this improved filtering strategy for accurate estimation of point spread function
(PSF), and hence, a more precise image restoration. As a result, the proposed solutions
compare favourably against existing techniques with the peak signal-to-noise ratio
(PSNR), kernel similarity (KS) index, and error ratio (ER) averaging at 24.8 dB, 0.6
and 1.4, respectively. Additional experiments involving real blurred images
demonstrated the competitiveness of the proposed approach in performing restoration
in the absent of PSF.