Wavelet Based Intentional Blurring Variance Scheme for Blur Detection in Barcode Images
Author(s) -
Shamik Tiwari,
V. P. Shukla,
S. R. Biradar,
Ajay Kumar Singh
Publication year - 2014
Publication title -
international journal of image graphics and signal processing
Language(s) - English
Resource type - Journals
eISSN - 2074-9082
pISSN - 2074-9074
DOI - 10.5815/ijigsp.2014.06.06
Subject(s) - barcode , artificial intelligence , computer vision , robustness (evolution) , computer science , wavelet , deblurring , image restoration , image (mathematics) , gaussian blur , metric (unit) , motion blur , pattern recognition (psychology) , image processing , engineering , biochemistry , chemistry , operations management , gene , operating system
Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation. Automatic blur detection is extremely enviable to restore barcode image or simply utilize them. That is to assess whether a given image is blurred or not. To detect blur, many algorithms have been proposed. These algorithms are different in their performance, time complexity, precision, and robustness in noisy environments. In this paper, we present an efficient method blur detection in barcode images, with no reference perceptual blur metric using wavelets. Index Terms—Blur, Intentional blur detection, Wavelet, Barcode images.
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