Single image defocus blur segmentation using Local Ternary Pattern

Muhammad Tariq Mahmood

doi:10.1016/j.icte.2019.10.003

2020-06-01

Muhammad Tariq Mahmood

Usman Ali

Young Choi *

DOI：10.1016/j.icte.2019.10.003

原文链接

查看原文

摘要

En 中文

This work presents an efficient LTP-based sharpness measure for blur detection and segmentation. The proposed method transforms each pixel into ternary codes depending on the differences of intensity of the central pixel with the neighborhood pixels. These ternary codes have been converted into lower and upper binary patterns. Among these, the non-uniform patterns have been exploited to compute the blur measure and blur segmentation. The proposed methodology performs segmentation without having any explicit information about the type and level of the blur. Experimental results reveal that the proposed method outperforms the state-of-the-art blur detection and segmentation methods. (C) 2020 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.

Keyword:

Blur measure

Blur segmentation

Local Ternary Patterns (LTP)