Paper Info
Title
Blur Map Generation Based On Local Natural Image Statistics For Partial Blur Segmentation
Abstract
Partial blur segmentation is one of the most interesting topics in computer vision, and it has practical value. The generation of blur maps is a crucial part of partial blur segmentation because partial blur segmentation involves producing a blur map and applying a segmentation algorithm to the blur map. In this study, we address two important issues in order to improve the discrimination of blur maps: (1) estimating a robust local blur feature to consider variations in the intensity amplitude and (2) a scheme for generating blur maps. We propose the ANGHS (Amplitude-Normalized Gradient Histogram Span) as a local blur feature. ANGHS represents the heavy-tailedness of a gradient distribution, where it is calculated from an image gradient normalized using the intensity amplitude. ANGHS is robust to variations in the intensity amplitude, and it can handle local regions in a more appropriate manner than previously proposed local blur features. Blur maps are affected by local blur features but also by the contents and sizes of local regions, and the assignment of blur feature values to pixels. Thus, multiple-sized grids and the EAI (Edge-Aware Interpolation) are employed in each task to improve the discrimination of blur maps. The discrimination of the generated blur maps is evaluated visually and statistically using numerous partial blur images. Comparisons with the results obtained by state-of-the-art methods demonstrate the high discrimination of the blur maps generated using the proposed method.
Year
DOI
Venue
2017
10.1587/transinf.2017EDP7119
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
ANGHS, blur map generation, natural image statistics, partial blur segmentation
Computer vision,Pattern recognition,Segmentation,Computer science,Artificial intelligence
Journal
Volume
Issue
ISSN
E100D
12
1745-1361
Citations 
PageRank 
References 
0
0.34
10
Authors
2
Name
Order
Citations
PageRank
Natsuki Takayama112.38
Hiroki Takahashi210.70