DocumentCode :
3564445
Title :
No-reference multi-scale blur metric
Author :
Harrity, Kyle ; Ezekiel, Soundararajan ; Ferris, Michael ; Cornacchia, Maria ; Blasch, Erik
Author_Institution :
Indiana Univ. of Pennsylvania, Indiana, PA, USA
fYear :
2014
Firstpage :
103
Lastpage :
108
Abstract :
In recent years, digital cameras have been widely used for image capturing. These devices are equipped in cell phones, laptops, tablets, webcams, etc. Image quality is an important characteristic for any digital image analysis. Historically, techniques to assess image quality for these mobile products require a standard image to be used as a reference image. In this case, Root Mean Square Error and Peak Signal to Noise Ratio can be employed to measure the quality of the images. However, these methods are not valid if there is no reference image. Recent studies show that a Contourlet is a multi-scale transformation - which is an extension of two dimensional wavelet transformations - that can operate on an image at different noise levels without a reference image. In this paper, we develop a no-reference blur metric for digital images based on edges and noises in images. In our approach, a Contourlet transformation is applied to the blurred image, which applies a Laplacian Pyramid and Directional Filter Banks to get various image representations. The Laplacian Pyramid is a difference of Gaussian Pyramids between two consecutive levels. At each level in the Gaussian Pyramid, an image is smoothed with two Gaussians of different sizes then subtracted, subsampled and the input image is decomposed into directional sub-bands of images. Directional filter banks are designed to capture high frequency components representing directionality of the images which is similar to detailed coefficient in wavelet transformation. We focus on blur-measuring for each level and directions at the finest level of images to assess the image quality. Using the ratio of blur pixels to total pixels, we compare our results, which require no reference image, to standard full-reference image statistics. The results demonstrate that our proposed no reference metric has an increasing relationship with the blurriness of an image and is more sensitive to blur than the correlation full-reference metric.
Keywords :
cameras; channel bank filters; image capture; image filtering; image representation; image restoration; laptop computers; matrix decomposition; mean square error methods; mobile handsets; notebook computers; wavelet transforms; Gaussian pyramid; Laplacian pyramid; cell phone; contourlet transformation; digital camera; digital image analysis; directional filter bank; image blurriness; image capturing quality; image decomposition; image representation; image smoothing; laptop device; no-reference multiscale blur metric; peak signal to noise ratio; reference metric; root mean square error; standard full-reference image statistics; tablet device; two dimensional wavelet multiscale transformation; webcam device; Correlation; Filter banks; Image edge detection; Image quality; Laplace equations; Measurement; Transforms; Blur; Contourlet transform; Directional Filter Bank; Laplacian Pyramid; Multi-scale; Noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Aerospace and Electronics Conference, NAECON 2014 - IEEE National
Print_ISBN :
978-1-4799-4690-7
Type :
conf
DOI :
10.1109/NAECON.2014.7045786
Filename :
7045786
Link To Document :
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