Abstract  This  paper  presents  a  simple  and  effective  method  f or  image  contrast  enhancement  called  spatially  weig hted  histogram  equalization.  Spatially  weighted  histogra m  not  only  considers  the  times  of  each  grey  value  a ppears  in  a  certain  image,  but  also  takes  the  local  characteristics  of  each  pixel  into  account.  In  the homogeneous  region  of  an  image,  the spatial  weights  of  pixels  tend  to  zero,  whereas  at  the  edge s  of  the  image,  this  weights  are  very  large.  In  order  to  maintain  the  mean  brightness of the original image, the grey level tr ansformation function calculated by spatial weighte d histogram equalization  is  modified,  and  the  final  result  is  given  by  mappi ng  the  original  image  through  this  modified  grey  le vel  transformation  function.  The  experimental  results  show  that  the  pr oposed  method  has  better  performance  than  the  exist ing  methods,  and  preserve the original brightness quite well, so tha t it is possible to be utilized in consumer electronic products.   

References

[1] Chen D. and Ramli R., Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement, IEEE Transaction Consumer Electronics , vol. 49, no. 4, pp. 1310- 1319, 2003.

[2] Chen D. and Ramli R., Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation, IEEE Transaction Consumer Electronics , vol. 49, no. 4, pp. 1301-1309, 2003.

[3] Canny J., A Computational Approach to Edge Detection, IEEE Transaction Pattern Analysis and Machine Intelligence , vol. PAMI-8, no. 6, pp. 679-698, 1986.

[4] Gonzalez C. and Woods E., Digital Image Processing , Prentice Hall, 2002.

[5] Kabir H., Al-Wadud A., and Chae O. , Brightness Preserving Image Contrast Enhancement using Weighted Mixture of Global and Local Transformation Functions, International Arab Journal of Information Technology , vol. 7, no. 4, pp. 403-410, 2010.

[6] Kim Y., Contrast Enhancement using Brightness Preserving Bi-Histogram Equalization, IEEE Transaction Consumer Electronics , vol. 43, no. 1, pp. 1-8, 1997.

[7] Matkovic K., Neumann L., Neumann A., Psik T., and Purgathofer W., Global Contrast Factor-a New Approach to Image Contrast, in Proceedings of Computational Aesthetics in Graphics, Visualization and Imaging , pp.159- 168, 2005.

[8] Umbaugh E., Computer Vision and Image Processing , Prentice Hall, New Jersey, 1998.

[9] Wan Y., Chen Q., and Zhang B., Image Enhancement Based on Equal Area Dualistic Sub-Image Histogram Equalization Method, IEEE Transaction Consumer Electronics , vol. 45, no. 1, pp. 68-75, 1999.

[10] Zuo C., Chen Q., Liu N., Ren J., and Sui X., Display and Detail Enhancement for High- Dynamic-Range Infrared Images, Optical Engineering , vol. 50, no. 12, pp. 127401-127409, 2011.

[11] Zuo C., Chen Q., and Sui X., Range Limited Bi- Histogram Equalization for Image Contrast Enhancement, Optik-International Journal for Light and Electron Optics , vol. 124, no. 5, pp. 425-431, 2013. Chao Zuo received his BS degree from Nanjing University of Science and Technology, P.R. China, in 2009. He is currently pursuing his PhD degree in the School of Electronic Engineering and Optoelectronic Techniques, Nanjing University of Science and Technology, P.R. China. H is research interests include signal and image process ing, algorithms for infrared spectral sensors and imager s, digital holography and 3-D shape measurement. He is student member of OSA and SPIE. Qian Chen received his BS, MS, and PhD degree from the School of Electronic Engineering and Optoelectronic Techniques, Nanjing University of Science and Technology. He is currently a professor and the dean of the Department of Optoelectronic Technology, Nanjing University of Science and Technology. He has led many research projects and authored more than 100 journal papers. His works have covered different topics, such as real-time digital image processing, optoelectronic imaging, electro-optical displaying technology, optoelectronic signal processing and transmission. He is member of SPIE and SID. 32 The International Arab Journal of Information Technology, Vol. 11, No. 1, January 2014 Xiubao Sui received his PhD degree in optical engineering from the School of Electronic Engineering and Optoelectronic Techniques, Nanjing University of Science and Technology. Now his research interests include the driver of infrared focal plane arrays and the theory research of infrared detectors. Jianle Ren received his BS degree from Nanjing University of Science and Technology, in 2009. He is a PhD candidate in optical engineering from the School of Electronic Engineering and Optoelectronic Techniques, Nanjing University of Science and Technology. He is interested in infrare d image processing and image fusion.