Abstract
Knowledge is hidden in images in form of objects, structures, patterns and their relationships, which are acquired through devices associated with various artifacts including blurring and noise. This paper presents a model-independent method for local blur-scale estimation based on a novel hypothesis that gradients inside a blur-scale region follow a Gaussian distribution with non-zero mean. New statistical test criteria involving maximal likelihood functions are presented to test the hypothesis and applied for blur-scale estimation. Also, the applications of blur-scale for scale-based gradient and edge computation are presented. In the context of scale-based edge computation, new methods are introduced to suppress false gradient maxima avoiding double edging artifacts. New methods are examined on computer-generated as well as real-life images with varying blur and noise. Experimental results show that computed blur-scale using the new algorithm is accurate (r = 0.95) and scale-based gradients are visually satisfactory at both sharp as well as blurred edge locations. Performance of the new edge detection algorithm is quantitatively examined and compared with two popular methods, and the results show that, at various contrast-to-noise ratio, the new method is superior to the others in terms of overall accuracy (92 to 96%), true edge detection (96 to 98%), and false edge reduction (93 to 100%).
Similar content being viewed by others
Data Availability
Data sharing not applicable to this article as no dataset were generated or analyzed during the current study.
References
Abràmoff M D, Magalhães P J, Ram S J (2004) Image processing with imagej. Biophoton Int 11(7):36–42
Bahrami K, Kot A C (2016) Efficient image sharpness assessment based on content aware total variation. IEEE Trans Multimed 18(8):1568–1578
Bao P, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Mach Intell 27(9):1485–1490
Bergholm F (1987) Edge focusing. IEEE Trans Pattern Anal Mach Intell, 726–741
Canny J (1986) A computational approach to edge detection. IEEE Transaction on Pattern Analysis and Machine Intelligence, 679–698
Deselaers T, Keysers D, Ney H (2008) Features for image retrieval: an experimental comparison. Inf Retrieval 11(2):77–107
Elder J H, Zucker S W (1998) Local scale control for edge detection and blur estimation. IEEE Trans Pattern Anal Mach Intell 20(7):699–716
Guha I, Saha P K (2018) A new algorithm for local blur-scale computation and edge detection. In: International symposium on visual computing. Springer, pp 598–606
Jeong H, Kim C I (1992) Adaptive determination of filter scales for edge detection. IEEE Trans Pattern Anal Mach Intell 14(5):579–585
Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vision 1:321–331
Li L, Lin W, Wang X, Yang G, Bahrami K, Kot A C (2015) No-reference image blur assessment based on discrete orthogonal moments. IEEE Trans Cybern 46(1):39–50
Li L, Xia W, Lin W, Fang Y, Wang S (2016) No-reference and robust image sharpness evaluation based on multiscale spatial and spectral features. IEEE Trans Multimed 19(5):1030–1040
Li L, Zhou Y, Gu K, Yang Y, Fang Y (2019) Blind realistic blur assessment based on discrepancy learning. IEEE Trans Circuits Syst Video Technol 30(11):3859–3869
Lindeberg T (2013) Scale-space theory in computer vision, vol 256. Springer Science & Business Media
Lowe D G (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110
Maesschalck R D, Jouan-Rimbaud D, Massart D L (2000) The mahalanobis distance. Chemom Intell Lab Syst 50(1):1–18
Magnier B (2018) Edge detection: a review of dissimilarity evaluations and a proposed normalized measure. Multimed Tools Applic 77(8):9489–9533
Marr D, Hildreth E (1980) Theory of edge detection. Proc R Soc London 207:187–217
Marziliano P, Dufaux F, Wnkler S, Ebrahimi T (2002) A no-reference perceptual blur metric. In: Proceedings of the international conference on image processing, vol 3, Rochester, pp 57–60
Meijering H W, Niessen W J, Viergever M A (2001) Quantitative evaluation of convolution-based methods for medical image interpolation. Med Image Anal 5:111–126
Netzer C, Monstavicius D, Capsi A Elder zucker image compression: implementation & analysis. https://web.stanford.edu/class/ee368b/Projects/cnetzer/index.html. Accessed 1 March 2023
Otsu N (1979) A threshold selection methods from grey-level histograms. IEEE Trans Pattern Anal Mach Intell 9:62–66
Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F et al (2015) Image database tid2013: peculiarities, results and perspectives. Signal Process: Image Commun 30:57–77
Prautzsch H, Boehm W, Paluszny M (2002) Bézier and b-spline techniques. Springer Science & Business Media
Saha P K, Gomberg B R, Wehrli F W (2000) Three-dimensional digital topological characterization of cancellous bone architecture. Int J Imaging Syst Technol 11:81–90
Saha P K, Zhang H, Udupa J K, Gee J C (2003) Tensor scale-based image registration. In: Medical imaging 2003: image processing, vol 5032. SPIE, pp 314–324
Saha P K, Borgefors G, Sanniti di Baja G (2016) A survey on skeletonization algorithms and their applications. Pattern Recogn Lett 76:3–12
Saha P K, Jin D, Liu Y, Christensen G E, Chen C (2017) Fuzzy object skeletonization: theory, algorithms, and applications. IEEE Trans Visual Comput Graph 24(8):2298–2314
Shapiro S S, Wilk M B (1965) An analysis of variance test for normality (complete samples). Biometrika 52(3/4):591–611
Sheikh H R, Sabir M F, Bovik A C (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15(11):3440–3451
Sonka M, Hlavac V, Boyle R (2014) Image processing, analysis, and machine vision. Cengage Learning
Strand R, Ciesielski K C, Malmberg F, Saha P K (2013) The minimum barrier distance. Comput Vis Image Underst 117:429–437
Udupa J K, Saha P K, Lotufo R A (2002) Disclaimer: relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. IEEE Trans Pattern Anal Mach Intell 24(11):1485–1500
Wang Z, Simoncelli E P (2004) Local phase coherence and the perception of blur. In: Advances in neural information processing systems, vol 16. MIT Press, Cambridge, pp 1435–1442
Xu Z, Sonka M, Saha P K (2011) Improved tensor scale computation with application to medical image interpolation. Comput Med Imaging Graph 35(1):64–80
Zhang X, Chen C, Boone S, Joshi V, Welbeck A, Liang G, Chang G, Saha P K (2018) Mri-based active shape model of the human proximal femur using fiducial and secondary landmarks and its validation. In: Medical imaging 2018: biomedical applications in molecular, structural, and functional imaging, vol 10578. International Society for Optics and Photonics, pp 1–12
Zhuo S, Sim T (2011) Defocus map estimation from a single image. Pattern Recogn 44(9):1852–1858
Acknowledgements
This work was supported by the NIH grants R01-HL142042.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
All authors state that they have no conflicts of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Guha, I., Saha, P. A model-independent method for local blur estimation and its application to edge detection. Multimed Tools Appl 82, 25779–25793 (2023). https://doi.org/10.1007/s11042-023-14779-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-14779-2