A Fog-Removing Treatment Based on Combining High-Frequency Emphasis Filtering and Histogram Equalization

Article Preview

Abstract:

Fog may cause the impairment of image as well as the decrease in the distinguish ability. The present paper is to get rid of the weather’s influence from the impaired image. According to Retinex theory and exponential relationship between the degradation of the image and the depths of the scene points, it puts forward a fog-removing treatment based on combining high-frequency emphasis filtering and histogram equalization .Firstly, obtain the padding parameters and fill it. Secondly, filter the impaired image using Butterworth highpass filter of order 2. Through the padding parameters, Highpass filtering is not overly sensitive to the value of cutoff frequencies, as long as the radius of the filter is not so small that frequencies near the origin of the transform are passed. In which the gray-level tonality due to the low-frequency components was retained. Lastly, histogram balanced the image gotten last step. The simulation result based on Matlab shows his algorithm can effectively improve the visual effect scene under the condition of mist.

You might also be interested in these eBooks

Info:

Periodical:

Key Engineering Materials (Volumes 474-476)

Pages:

2198-2202

Citation:

Online since:

April 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] McCartney. EJ: Optics of Atmosphere: Scattering by Molecules and Particles. New York: John Wiley and Sons (1976), pp.23-32.

Google Scholar

[2] Cozman. F, Krotkov. E: Depth from scattering. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico (1997), pp.801-806.

DOI: 10.1109/cvpr.1997.609419

Google Scholar

[3] NS. Kopeika: A system Engineering Approach to imaging. Spie Press (1981).

Google Scholar

[4] Narasimhan. SG, Nayar. SK: Vision and atmosphere. International Journal of Computer vision, Vol. 48(3) 2002, pp.233-254.

Google Scholar

[5] Narasimhan. SG, Nayar. SK: Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25(6) 2003, pp.713-724.

DOI: 10.1109/tpami.2003.1201821

Google Scholar

[6] ZHU. Pei, ZHU. Hong: An Image Clearness Method for Fog. Journal of Image and Graphics, Vol. 19(1) 2004, pp.124-127.

Google Scholar

[7] Land. E: An alternative technique for the computation of the designator in the retinex theory of color vision. Proc. Acad. Sci, Vol. 83(10) 1986, pp.3078-3080.

DOI: 10.1073/pnas.83.10.3078

Google Scholar

[8] Hurlbert. AC: Formal connections between lightness algorithms. Journal of the Optical Society of America, Vol. 3(10) 1986, pp.1684-1693.

DOI: 10.1364/josaa.3.001684

Google Scholar

[9] Richard. E, Rafaek. C, Stenven. L: Digital Image Processing Using MATLAB. Prentice Hall (2004), pp.84-85.

Google Scholar