Low illumination image Retinex enhancement algorithm based on guided filtering

It has been shown by Retinex theory that the object's color in the image is determined by its reflectance. Under this frame work, several Retinex-based algorithms have been developed to uncover the real appearance of objects by estimating their reflectance in image. In this paper we propose an algorithm of optimizing the illumination based on guided image filter theory, where bilateral filter is utilized to estimate the illumination image. The proposed algorithm removes the halo phenomenon precisely and strengthens the partial contrast gradient effectively. Examples are given to demonstrate the effectiveness of the proposed method.

[1]  Jin Weiqi,et al.  Global Color Image Enhancement Algorithm Based on Retinex Model , 2010 .

[2]  H. Yeganeh,et al.  A novel approach for contrast enhancement based on Histogram Equalization , 2008, 2008 International Conference on Computer and Communication Engineering.

[3]  Wang Xiaoming.  Improved multi-scale retinex image enhancement algorithm , 2010 .

[4]  Greg Turk,et al.  LCIS: a boundary hierarchy for detail-preserving contrast reduction , 1999, SIGGRAPH.

[5]  Michael Elad,et al.  Retinex by Two Bilateral Filters , 2005, Scale-Space.

[6]  Geoffrey D. Rubin,et al.  Adaptive border marching algorithm: Automatic lung segmentation on chest CT images , 2008, Comput. Medical Imaging Graph..

[7]  A. Hurlbert.  The Computation of Color , 1989 .

[8]  Nam Chul Kim,et al.  Color Image Enhancement Based on Single-Scale Retinex With a JND-Based Nonlinear Filter , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[9]  Chang Huang,et al.  Improved multi-scale retinex image enhancement algorithm: Improved multi-scale retinex image enhancement algorithm , 2010 .

[10]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  O. Faugeras.  Digital color image processing within the framework of a human visual model , 1979 .

[12]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

[13]  Michael Elad,et al.  A Variational Framework for Retinex , 2002, IS&T/SPIE Electronic Imaging.

[14]  Tomaso Poggio,et al.  Synthesizing a color algorithm from examples , 1988 .

[15]  Xia De-shen.  Single-scale Retinex Image Enhancement Based on Bilateral Filtering , 2009 .

[16]  Hu Qiong.  Retinex Algorithm for Image Enhancement Based on Bilateral Filtering , 2010 .

[17]  Donghui Guo,et al.  A Halo-Free and Hue Preserving Algorithm for Color Image Enhancement: A Halo-Free and Hue Preserving Algorithm for Color Image Enhancement , 2010 .

[18]  Ashok T. Amin.  An Algorithm for Grey-Level Transformations in Digitized Images , 1977, IEEE Transactions on Computers.

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

[20]  A Hurlbert,et al.  Formal connections between lightness algorithms. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[21]  Jin Xiao-mei.  Application of Self-adaptive Histogram Equalization Algorithm to Image Enhancement Processing , 2010 .

[22]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[23]  Laurence Meylan,et al.  High dynamic range image rendering with a retinex-based adaptive filter , 2006, IEEE Transactions on Image Processing.

[24]  Doron Shaked.  Interpolation for nonlinear Retinex-type algorithms , 2007, Electronic Imaging.

[25]  Bo Li,et al.  A fast Multi-Scale Retinex algorithm for color image enhancement , 2008, 2008 International Conference on Wavelet Analysis and Pattern Recognition.

[26]  T. Poggio,et al.  Synthesizing a color algorithm from examples. , 1988, Science.

[27]  Baoguo Xu,et al.  An Image Enhancement Approach Using Retinex and YIQ , 2009, 2009 International Conference on Information Technology and Computer Science.