%0 Journal Article
%T A Blind Blur Detection Scheme Using Statistical Features of Phase Congruency and Gradient Magnitude
%A Shamik Tiwari
%A V. P. Shukla
%A S. R. Biradar
%A A. K. Singh
%J Advances in Electrical Engineering
%D 2014
%R 10.1155/2014/521027
%X The growing uses of camera-based barcode readers have recently gained a lot of attention. This has boosted interest in no-reference blur detection algorithms. Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation. In this paper we present a new no-reference blur detection scheme that is based on the statistical features of phase congruency and gradient magnitude maps. Blur detection is achieved by approximating the functional relationship between these features using a feed forward neural network. Simulation results show that the proposed scheme gives robust blur detection scheme. 1. Introduction Barcodes are commonly used system of encoding of machine understandable information on most commercial products and services [1]. Two-dimensional (2D) barcodes have higher density, capacity, and reliability than one-dimensional (1D) barcodes. Therefore, 2D barcodes have been progressively more adopted these days. For example, a consumer can access essential information from the web page of the magazine or book, when he reads it, by just capturing the image of the printed QR code (2D barcode) related to URL. In addition to the URLs, 2D barcodes can also symbolize visual tags in the supplemented real-world environment [2], and there exists the adaptation from the individual profiles to 2D barcodes. Whereas 1D barcodes are traditionally scanned with laser scanners, 2D barcode symbologies need imaging device for scanning. Detecting bar codes from images taken by digital camera is particularly challenging due to different types of degradations like geometric distortion, noise, and blurring in image at the time of image acquisition. Image blurring [3] is frequently an issue that affects the performance of a barcode identification system. Blur may arise due to diverse sources like atmospheric turbulence, defocused lens, optical abnormality, and spatial and temporal sensor assimilation. Two common types of blurs are motion blur and defocus blur. Motion blur is caused by the relative motion between the camera and object during image capture while the defocus blur is caused by the inaccurate focal length adjustment at the time of image capturing. Blurring induces the degradation of image sharpness like edges, specifically for barcode images where the encoded information is easily lost due to blurring. Human visual system has good capability to perceive blur. However, the mechanism behind this capability is not completely understood for application in artificial visual systems. Therefore, it is hard to design a metric for
%U http://www.hindawi.com/journals/aee/2014/521027/