Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15] [ 16] [ 17] [ 18] [ 19] [ 20] [ 21]


Journal of Information Science and Engineering, Vol. 30 No. 3, pp. 605-618 (May 2014)

Evolutionary Design of Evolvable Hardware Image Filters Using Fuzzy Noise Models*

1Department of Electrical Engineering
National University of Kaohsiung
Kaohsiung, 811 Taiwan
2Intelligent Systems Department
Infochamp Systems Corporation
Kaohsiung, 806 Taiwan

Image filtering, which removes or reduces noise from contaminated images, is an important task in image processing. This study deals with evolutionary design of image filters that can be implemented on evolvable hardware platforms using fuzzy noise models. Two fuzzy sets, similarity and divergence, are defined for classifying noise. Three filtering modules for pixels with various degrees of noise contamination are trained supervisedly by Cartesian genetic programming. The recovery of a noisy pixel is the fuzzy weighted sum of the output from the three filtering modules. Because each image filter is dedicated to a specific type of noise, it can produce a more accurate value for pixel recovery. With the proposed method, better accuracy of image filtering can be obtained. This paper evaluates and compares the performance of our proposed method with other ones.

Keywords: evolvable hardware, Cartesian genetic programming, image filter, fuzzy sets, salt and pepper noise

Full Text () Retrieve PDF document (201405_04.pdf)

Received February 28, 2013; accepted June 15, 2013.
Communicated by Hung-Yu Kao, Tzung-Pei Hong, Takahira Yamaguchi, Yau-Hwang Kuo, and Vincent Shin- Mu Tseng.
* This work was partially supported by National Science Council, Taiwan, under Grant No. NSC 100-2221-E- 390-029.
+ Corresponding author.