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A Simple Second Derivative Based Blur Estimation Technique Thesis A Simple Second Derivative Based Blur Estimation Technique Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Gourab Ghosh Roy, B.E. Graduate Program in Computer Science & Engineering The Ohio State University 2013 Thesis Committee: Brian Kulis, Advisor Mikhail Belkin Copyright by Gourab Ghosh Roy 2013 Abstract Blur detection is a very important problem in image processing. Different sources can lead to blur in images, and much work has been done to have automated image quality assessment techniques consistent with human rating. In this work a no-reference second derivative based image metric for blur detection and estimation has been proposed. This method works by evaluating the magnitude of the second derivative at the edge points in an image, and calculating the proportion of edge points where the magnitude is greater than a certain threshold. Lower values of this proportion or the metric denote increased levels of blur in the image. Experiments show that this method can successfully differentiate between images with no blur and varying degrees of blur. Comparison with some other state-of-the-art quality assessment techniques on a standard dataset of Gaussian blur images shows that the proposed method gives moderately high performance values in terms of correspondence with human subjective scores. Coupled with the method’s primary aspect of simplicity and subsequent ease of implementation, this makes it a probable choice for mobile applications. ii Acknowledgements This work was motivated by involvement in the personal analytics research group with Dr. Mikhail Belkin, Dr. Simon Dennis, Dr. Jihun Hamm and other group members. iii Vita 2004 ...............................................................Nava Nalanda High School 2006................................................................South Point High School 2010................................................................B.E. Electronics & Tele-Communication Engineering, Jadavpur University 2010-present.............................................. Fellow/Graduate Teaching Associate/ Graduate Research Associate, Computer Science and Engineering Department, The Ohio State University Publications G. G. Roy, S. Das, P. Chakraborty, and P.N. Suganthan. “Design of Non-uniform Circular Antenna Arrays using a Modified Invasive Weed Optimization Algorithm”. iv IEEE Transactions on Antennas and Propagation, vol. 59, Issue 1, pp. 110-118, Jan. 2011. P. Chakraborty, G. G. Roy, S. Das, D. Jain and A. Abraham. “An Improved Harmony Search Algorithm with Differential Mutation Operator”. Fundamenta Informaticae Journal, vol. 95, Issue 4, pp. 401-426, Dec. 2009. G. G. Roy, P. Chakraborty, S. Das. “Designing Fractional-order PIλDμ Controller Using Differential Harmony Search Algorithm”. International Journal of Bio-Inspired Computation, vol. 2, No. 5, pp. 303-309, Oct. 2010. P. Chakraborty, G. G. Roy, B.K.Panigrahi, R.C.Bansal and A. Mohapatra. “Dynamic Economic Dispatch Using Harmony Search algorithm with Modified Differential Mutation Operator”. Electrical Engineering, vol. 94, Issue 4, pp. 197-205, Dec. 2012. T.K. Gandhi, P. Chakraborty, G. G. Roy and B.K.Panigrahi. “Discrete harmony search based Expert model for epileptic seizure detection in electroencephalography”. Expert Systems with Applications, vol. 39, Issue 4, pp. 4055-4062, March 2012. Fields of Study Major Field: Computer Science and Engineering v Table of Contents Abstract.............................................................................................................................. ii Acknowledgments............................................................................................................. iii Vita.................................................................................................................................... iv List of Tables.................................................................................................................... vii List of Figures................................................................................................................. viii Introduction………………………................................................................................... 1 Previous Work………………………………………………........................................... 3 Description of the method…………………..................................................................... 5 Results…………………................................................................................................... 9 Discussion……………..................................................................................................... 16 Conclusion…………........................................................................................................ 18 References........................................................................................................................ 19 vi List of Tables Table 1. Comparison of Spearman Rank Order Correlation Coefficient Values for LIVE database images with Gaussian blur..................................................................................14 vii List of Figures Figure 1. Plot of intensity along the vertical direction for the same edge point in the same image, without blur (red) and with blur (green)…………………………………………..7 Figure 2. (a) Original image (b) Image blurred with Gaussian filter of size 3*3, and sigma=6 (c) Image blurred with Gaussian filter of size 5*5, and sigma=10 (d) Image blurred with Gaussian filter of size 6*6, and sigma=12……………………………...….10 Figure 3. (a) Original image (b) Image blurred with Gaussian filter of size 3*3, and sigma=6 (c) Image blurred with Gaussian filter of size 5*5, and sigma=10 (d) Image blurred with Gaussian filter of size 6*6, and sigma=12……………………………........12 Figure 4. Bar graph corresponding to Table 1, numbering order of methods same as in the table, red bar denotes the proposed method………………………………....…………...15 viii Introduction Image quality assessment is a key concept in the field of image and video processing. Many computer vision applications deal with automated assessment of the perceptual quality of an image. Probably the best possible way to obtain such an assessment is to have humans provide subjective rating of the images, but even for a slightly large-scale application this is not a practical option. There lies the significance of such quality assessment methods. The objective is to have such methods mimic human quality assessment. Out of the several types of image distortions, one of the most common types is image blur. Several factors can lead to an image being blurred during capture. One such case to consider is the associated motion while capturing images with a wearable camera like one in a cell phone. There has been a lot of work in the field of blur detection and estimation. The no-reference blur estimation techniques which do not use any knowledge of the original image are of primary importance. In this thesis work, one such no-reference blur detection technique is proposed. It uses the notion of second derivative at edge points in an image. The report is organized as follows: First some idea of previous work in blur detection is given, followed by the description of the proposed method. Then the results of this 1 proposed method are presented which includes a comparison with some other blur detection techniques on standard dataset images with Gaussian blur. Then there is the discussion section where the significance of the proposed method is highlighted, followed by conclusion and references. 2 Previous Work Much work has been done in the field of blur detection. Broadly, the blur detection algorithms can be divided into three categories. One is the class of full-reference algorithms, which use information from the original reference image in estimating the amount of blur in the blurry image. The other category is the class of no-reference algorithms, which do not use any information of the reference image and give an absolute metric value for the blurry image. From the practical point of view, no-reference algorithms are more significant because the original reference image is not always available in real situations. There is also a third category of reduced-reference algorithms which use only part of the information from the reference image. Another important point to mention in discussing about previous work is the fact that analysis of edges in an image has been widely used in blur estimation. Edges are the high frequency components in an image and as such, are most affected by the process of blur. Here a brief overview of the quality assessment algorithms to which the proposed method has been compared in performance is given. In [1], the authors propose a new spectral moment autofocus measure. An image quality measure is developed from the digital image power spectrum of normally acquired arbitrary scenes in [2]. An edge based blur detection method was proposed in [3] where the authors create an edge profile by 3 surrounding edge pixels with blocks and use kurtosis of those blocks for sharpness measure. A wavelet-based sharpness metric with immunity to noise in the image was presented in [4]. A gradient based image quality assessment method using blur and noise was proposed in [5]. The authors of [6] used a Harr wavelet transform based blur detection scheme. [7] obtains improved results in blur detection by edge analysis through multi-resolution decomposition. [8] proposed image comparison
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