Proceedings of International Conference on Computer Vision and Image Processing pp 377-387 | Cite as
Design and Implementation of a Real-Time Autofocus Algorithm for Thermal Imagers
Abstract
Good image quality is the most important requirement of a thermal imager or any other imaging system in almost all applications. Degree of focus in an image plays a very important role in determining the image quality, thus focusing mechanism is a very important requirement in thermal imagers. A real-time and reliable passive autofocus algorithm has been developed and implemented in FPGA-based hardware. This autofocus module has been integrated with the video processing pipeline of thermal imagers. Prior to the hardware implementation, different algorithms for image sharpness evaluation have been implemented in MATLAB and simulations have been done with test video sequences acquired by a thermal imager with motorized focus control to analyze the algorithms efficiency. Cumulative gradient algorithm has been developed for image sharpness evaluation. The algorithm has been tested on images taken from a thermal imager under varying contrast and background conditions, and it shows high precision and good discriminating power. The images have been prefiltered by a median rank-order filter using a 3 × 3 matrix to make it more robust in handling noisy images. Complete autofocus algorithm design comprising of a frame acquisition module for acquiring user selectable central region in the incoming thermal imager video, Cumulative Gradient-based image sharpness evaluation module, fixed step size search-based focal plane search module and a motor pulse generation module for generating motor drives have been implemented on Xilinx FPGA device XC4VLX100 using Xilinx ISE EDA tool.
Keywords
Autofocus Gradient IR FPGA VHDL MATLAB NUC DRCNotes
Acknowledgments
We would like to thank Dr. S S Negi, Director, I.R.D.E for allowing us to work in this area.
References
- 1.R.D. Hudson, Infrared Systems Engineering, John Wiley, New York, 1969.Google Scholar
- 2.Gregory A. Baxes, “Digital Image Processing- Principles and Applications”, 1994.Google Scholar
- 3.J. Baina and J. Dublet, “Automatic focus and iris control for video cameras”, Fifth International Conf. on Image Processing and its Applications, pp. 232–235, July 1995.Google Scholar
- 4.Mark Antunes and Michael Trachtenberg, “All-In-Focus Imaging From A Series Of Images On Different Focal Planes”, B.Sc thesis report, Faculty of Engineering, University of Manitoba, March 2005.Google Scholar
- 5.Feng Li, Hong Jin, “A Fast Auto Focusing Method For Digital Still Camera”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18–21 August 2005.Google Scholar
- 6.Mukul V. Shirvaikar, “An Optimal Measure for Camera Focus and Exposure” Proceedings of the IEEE SSST 2004.Google Scholar
- 7.Ng Kuang Chern, Nathaniel Poo Aun Neow and Marcelo H. Ang Jr., “ Practical issues in pixel-based autofocusing for machine vision”, Proceedings of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, May 21–26, 2001 M.Google Scholar
- 8.Chun-Hung Shen and Homer H. Chen, “Robust Focus Measure for Low-Contrast Images”, Proceedings of the IEEE SSST 2006.Google Scholar
- 9.MATLAB version 2012.Google Scholar
- 10.J. Bhasker, A VHDL Primer, AT&T, 1999.Google Scholar