Hue-preserving color image enhancement using particle swarm optimization

Image enhancement is aimed to improve image quality by maximizing the information content in the input image. In this article a PSO based hue preserving color image enhancement technique is proposed. The process is as follows. Image enhancement is considered as an optimization problem and particle swarm optimization (PSO) is used to solve it. The quality of the intensity image is improved by a parameterized transformation function, in which parameters are optimized by PSO based on an objective function. The intensity transformation function uses local and global information of the input image and the objective function considers the entropy and edge information to measure the image quality. The enhanced color image is then obtained by scaling, which sometimes leads to gamut problem for few pixels. Rescaling is done to the saturation component to remove the gamut problem. The algorithm is tested on several color images and results are compared with two other popular color image enhancement techniques like hue-preserving color image enhancement without gamut problem (HPCIE) and a genetic algorithm based approach to color image enhancement (GACIE). Visual analysis, detail and background variance of the resultant images are reported. It has been found that the proposed method produces better results compared to other two methods.

[1]  F. Saitoh.  Image contrast enhancement using genetic algorithm , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[2]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[3]  Robin N. Strickland,et al.  Digital Color Image Enhancement Based On The Saturation Component , 1987 .

[4]  Guillermo Sapiro,et al.  Color image enhancement via chromaticity diffusion , 2001, IEEE Trans. Image Process..

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[7]  Vasile Lazarescu,et al.  EVOLUTIONARY CONTRAST STRETCHING AND DETAIL ENHANCEMENT OF SATELLITE IMAGES , 1999 .

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  Paul L. Rosin.  Edges: saliency measures and automatic thresholding , 1997, Machine Vision and Applications.

[10]  I. M. Bockstein.  Color equalization method and its application to color image processing , 1986 .

[11]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[12]  Jin-Jang Leou,et al.  A genetic algorithm approach to color image enhancement , 1998, Pattern Recognit..

[13]  Guo Run-qiu.  The infrared image enhancement and the correlative techniquebased on the parallel genetic algorithm , 2004 .

[14]  Alexander Toet.  A hierarchical morphological image decomposition , 1990, Pattern Recognit. Lett..

[15]  C. A. Murthy,et al.  Hue-preserving color image enhancement without gamut problem , 2003, IEEE Trans. Image Process..

[16]  Riccardo Poli,et al.  Evolution of Pseudo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming , 1997 .

[17]  Sanjit K. Mitra,et al.  Nonlinear unsharp masking methods for image contrast enhancement , 1996, J. Electronic Imaging.

[18]  Sankar K. Pal,et al.  Genetic algorithms for optimal image enhancement , 1994, Pattern Recognit. Lett..

[19]  Natalio Krasnogor,et al.  Studies on the theory and design space of memetic algorithms , 2002 .

[20]  Antonio Picariello,et al.  Multiscale contrast enhancement of medical images , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[21]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..