C15. Registration of multi-focus images using Hough transform

Image registration is the process of finding a transformation that aligns one image to another. Most researchers who are interested in the registration task don't take into consideration the out-of-focus phenomenon that might affect the accuracy of the registration process. Nevertheless, they suggest that the images are well focused. Consequently, this work deals with the registration of multi-focus images; the out of focus is being treated side by side with the registration process. This paper presents an accurate and fast method for rigid registration of multi focus images. It is based on Hough transformation of images followed by Particle Swarm Optimizer (PSO) as an optimization technique and mutual information (MI) as a cost function (similarity measure). Hough transform introduces a proper initialization for the optimizer. Maximizing the MI between reference and sensed images will lead to minimizing the misalignment error between reference and sensed images, hence yields to accurate registration.

[1]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[2]  Jacqueline Le Moigne.  Multi-Sensor Image Fusion and Its Applications , 2005 .

[3]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[4]  Yen-Wei Chen,et al.  Multimodal Medical Image Registration Using Particle Swarm Optimization , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[5]  William M. Wells,et al.  On the optimality of mutual information as an image registration objective function , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[6]  Gouda I. Salama,et al.  A no-reference blur metric guided fusion technique for multi-focus images , 2011, 2011 28th National Radio Science Conference (NRSC).

[7]  Jagath Samarabandu,et al.  Multi-modal image registration using line features and mutual information , 2010, 2010 IEEE International Conference on Image Processing.

[8]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[9]  Jacek M. Zurada,et al.  An approach to multimodal biomedical image registration utilizing particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.