A statistical adaptive algorithm for dust image enhancement and restoration

Analyses of images acquired in dusty environments show that the images tend to have noise, blur, small dynamic ranges, low contrast, diminished blue components, and high red components. The goal of this paper is to develop a strategy to enhance such dusty images using a sequence of image processing steps. A statistical adaptive algorithm consisting of image restoration using the Wiener filter, contrast stretching using the RGB color model, intensity stretching using the HSI color model, and color cast removal using color balance, is introduced. Enhancement experiments are conducted on real dusty images and it is shown that the strategy is quite effective in enhancing dusty images. Furthermore the results are superior to those obtained through histogram equalization, gray world, and white patch algorithms. In addition, the complexity of the proposed algorithm is very low thus making it attractive for real time-image processing.

[1]  Raimondo Schettini,et al.  Combining strategies for white balance , 2007, Electronic Imaging.

[2]  Rodney M. Goodman,et al.  A real-time neural system for color constancy , 1991, IEEE Trans. Neural Networks.

[3]  Brian V. Funt,et al.  Committee-Based Color Constancy , 1999, CIC.

[4]  Keigo Hirakawa,et al.  Color Constancy with Spatio-Spectral Statistics , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Graham D. Finlayson,et al.  Color by Correlation: A Simple, Unifying Framework for Color Constancy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Brian V. Funt,et al.  A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data , 2002, IEEE Trans. Image Process..

[7]  M. H. Brill,et al.  Color Appearance Models, 2nd Edition , 2005 .

[8]  Wen-Chung Kao,et al.  Color reproduction for digital imaging systems , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[9]  Yung-Cheng Liu,et al.  Automatic white balance for digital still camera , 1995 .

[10]  Shoji Tominaga,et al.  Surface Identification Using the Dichromatic Reflection Model , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Lidija Mandić,et al.  COLOUR APPEARANCE MODELS , 2002 .

[12]  G. Buchsbaum.  A spatial processor model for object colour perception , 1980 .

[13]  Yung-Cheng Liu,et al.  Automatic white balance of digital still camera , 1995, Proceedings of International Conference on Consumer Electronics.

[14]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[16]  Sos S. Agaian,et al.  Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Chiou-Shann Fuh,et al.  Automatic White Balance for Digital Still Cameras , 2006, J. Inf. Sci. Eng..

[18]  Ching-Chih Weng,et al.  A novel automatic white balance method for digital still cameras , 2005, 2005 IEEE International Symposium on Circuits and Systems.