An accurate Harmony Search based algorithm for the blind deconvolution of binary images

In this paper we introduce the Largest Error First Harmony Search (LEFHS) algorithm for the deconvolution, or deblurring, of binary images. The algorithm requires no prior information about the source of the blur which is commonly modelled as a point spread function (PSF). LEFHS is able to fully recover the original binary image even when the PSF is unknown making it a true blind deconvolution algorithm. LEFHS addresses a major limitation that is present in another blind deconvolution algorithm called CHS. CHS is also able to fully recover the original binary image but only when the blur source is restricted to a binary PSF. LEFHS does not have this restriction and successfully recovers images blurred by arbitrary PSFs. LEFHS addresses a significant restriction in CHS as non-binary PSFs are the norm in real images. We compare our results with CHS and other state of the art binary deconvolution algorithms and find that LEFHS is faster than CHS even when using more complex non-binary PSFs as well as being more accurate than other binary deconvolution algorithms.

[1]  Dhanesh Ramachandram,et al.  Dynamic fuzzy clustering using Harmony Search with application to image segmentation , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[2]  Tony F. Chan,et al.  Total variation blind deconvolution , 1998, IEEE Trans. Image Process..

[3]  Masaru Tanaka,et al.  Analysis on the Island Model Parallel Genetic Algorithms for the Genetic Drifts , 1998, SEAL.

[4]  J. Fourie,et al.  Counterpoint Harmony Search: An accurate algorithm for the blind deconvolution of binary images , 2010, 2010 International Conference on Audio, Language and Image Processing.

[5]  Edmund Y. Lam,et al.  Blind Bi-Level Image Restoration With Iterated Quadratic Programming , 2007, IEEE Transactions on Circuits and Systems II: Express Briefs.

[6]  Edmund Y. Lam,et al.  Robust Binary Image Deconvolution with Positive Semidefinite Programming , 2006 .

[7]  Yen-Wei Chen,et al.  A hybrid GA/SA approach to blind deconvolution , 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

[8]  Steven Mills,et al.  Directed correspondence search: Finding feature correspondences in images using the Harmony Search algorithm , 2009, 2009 24th International Conference Image and Vision Computing New Zealand.

[9]  E. Lam,et al.  Restoration of Binary Images Using Positive Semidefinite Programming , 2006, TENCON 2006 - 2006 IEEE Region 10 Conference.

[10]  Lam,et al.  Iterative statistical approach to blind image deconvolution , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[12]  Zong Woo Geem,et al.  Recent Advances In Harmony Search Algorithm , 2010, Recent Advances In Harmony Search Algorithm.

[13]  Steven Mills,et al.  Harmony filter: A robust visual tracking system using the improved harmony search algorithm , 2010, Image Vis. Comput..

[14]  J. Fourie,et al.  Visual tracking using the harmony search algorithm , 2008, 2008 23rd International Conference Image and Vision Computing New Zealand.

[15]  David S.C. Biggs.  Accelerated iterative blind deconvolution , 1998 .

[16]  Mila Nikolova,et al.  Finding the global minimum for binary image restoration , 2005, IEEE International Conference on Image Processing 2005.

[17]  Ngai Wong,et al.  Binary image restoration by positive semidefinite programming. , 2007, Optics letters.

[18]  Quan-Ke Pan,et al.  A local-best harmony search algorithm with dynamic subpopulations , 2010 .

[19]  Bogdan Artyushenko.  Analysis of global exploration of island model genetic algorithm , 2009, 2009 10th International Conference - The Experience of Designing and Application of CAD Systems in Microelectronics.

[20]  Daniel Cremers,et al.  Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming , 2003, IEEE Trans. Pattern Anal. Mach. Intell..