Abstract
An image enhancement algorithm based on a neighborhood dependent nonlinear model is presented to improve visual quality of digital images captured under extremely non-uniform lighting conditions. The paper presents techniques for adaptive and simultaneous intensity enhancement of extremely dark and bright images, contrast enhancement, and color restoration. The core idea of the algorithm is a nonlinear sine transfer function with an image dependent parameter. Adaptive computation of the control parameter increases flexibility in enhancing the dark regions and compressing overexposed regions in an image. A neighborhood dependent approach is employed for contrast enhancement. A linear color restoration process is used to obtain color image from the enhanced intensity image by utilizing the chromatic information of the original image. It is observed that the proposed algorithm yields visually optimal results on images captured under extreme lighting conditions, and also on images with bright and dark regions.
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References
Digital Photography Color Management Basics. International Color Consortium (April 2005), http://www.color.org/ICC_white_paper_20_Digital_photography_color_management_basics.pdf
Bianco, C.:Â How Vision Works (April 1, 2000), http://health.howstuffworks.com/eye.htm (January 13, 2010)
Kolb, H.: How the Retina Works. American Scientist 91(1) (2003)
Dynamic Range Compression, https://ccrma.stanford.edu/~jos/fp/Nonlinear_Filter_Example_Dynamic.html
Gonzalez, R., Woods, R.: Digital Image Processing, pp. 167–168. Addison Wesley Publishing Company, Reading (1992)
Bonnier, N., Simoncelli, E.P.: Locally Adaptive Multiscale Contrast Optimization. In: Proc. 12th IEEE International Conference on Image Processing, vol. 1, pp. 949–952 (2005)
Pizer, S.M., Amburn, E.P.: Adaptive histogram equalization and its variations. In: SPIE Proceedings, vol. 1092, pp. 290–300 (1989)
Funt, B., Ciurea, F., McCann, J.: Retinex in Matlab. In: Proc. CIC’8 8th Color Imaging Conference, Scottsdale, Arizona, pp. 112–121 (2000)
Land, E., McCann, J.: Lightness and Retinex Theory. Journal of the Optical Society of America 61(1) (1971)
Rahman, Z., Jobson, D., Woodell, G.: Multiscale Retinex for Color Image Enhancement. In: Proceedings of the IEEE International Conference on Image Processing. IEEE, Los Alamitos (1996)
Rahman, Z., Jobson, D., Woodell, G.: Multiscale Retinex for Color Rendition and Dynamic Range Compression. In: Tescher, A.G. (ed.) Proceedings of the SPIE Applications of Digital Image Processing XIX, vol. 2847 (1996)
Rahman, Z., Jobson, D., Woodell, G.: Multiscale Retinex for Bridging the Gap between Color Images and Human Observations of the Scene. IEEE Transactions on Image Processing: Special Issue on Color Processing 6, 965–976 (1997)
Rahman, Z., Woodell, G., Jobson, D.: A Comparison of the Multiscale Retinex with Other Enhancement Techniques. In: Proceeding of the IS&T 50th Anniversary Conference, IS&T, pp. 426–431 (1997)
Tao, L., Asari, K.V.: An Adaptive and Integrated Neighborhood Dependent Approach for Nonlinear Enhancement of Color Images. SPIE Journal of Electronic Imaging 14(4), 1.4 –1.14 (2005)
Arigela, S., Asari, K.V.: A Locally Tuned Nonlinear Technique for Color Image Enhancement. WSEAS Transactions on Signal Processing 4, 514–519 (2008)
Valensi, G.: Color Television System. US Patent 3, 534, 153 (October 1970)
Jobson, D.J., Rahman, Z., Woodell, G.: Statistics of Visual Representation. In: SPIE Proceedings, vol. 4736, pp. 25–35 (2002)
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Patel, R., Asari, V.K. (2010). A Neighborhood Dependent Nonlinear Technique for Color Image Enhancement. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13772-3_3
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DOI: https://doi.org/10.1007/978-3-642-13772-3_3
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