Research On Image Enhancement Based On Swarm Intelligence Algorithm

Swarm Intelligence Optimum Algorithm is widely used in many fields such as image processing, image analysis, image understanding and so on. Image enhancement techniques are very important to image processing, which are used to improve image quality or extract the fine details in the degraded images. The applications of Swarm Intelligence Optimum Algorithm including GA\PSO\QPSO in image enhancement are discussed in this paper.First of all, the advantages and disadvantages of every algorithm having been used in image enhancement are analyzed by looking up interrelated information on the basis of researching the main image enhancement algorithm and intelligence optimum algorithm. Image enhancement is formulated as an optimization problem in the paper and QPSO was first successfully used to solve image enhancement problems. The simulation results show that QPSO is superior to PSO. Not only parameter of QPSO is few and randomicity of QPSO is strong, but also QPSO covers with all solution space and guarantee global convergence of algorithms.Secondly, in process of researching image enhancement techniques, a new objective function is used to evaluate the performance of algorithm, and new adaptive function combined with intelligence optimum algorithm to enhance images. Using the approach, the optimization parameters in the normalized incomplete Beta function of degraded images can be automatically find out and can reason the degraded types of the original image correctly. The simulation results prove that the visual effects of degraded images are highly improved after enhancement. QPSO is also first successfully applied in gray-scale image enhancement. And the simulation results tell that the proposed approach has good performance in generalized exchange of intensity, and the image enhancement effect is prominent.Here it is assumed that each input-degraded color image is originally represented in the RGB color space, which is converted into the HIS color space for enhancement. The new objective function applied in intensity, adoption of equalization saturation and unchangeable of the hue component are used in the color image enhancement. The simulation results show that the enhanced image has the excellent visual effect.Finally, in QPSO, Contraction-Expansion Coefficient is a vital parameter to the convergence of individual particle in QPSO. Adaptive mechanism is used in this paper, therefore Adaptive Quantum-behaved Particle Swarm Optimization is used in the process of gray-scale degraded image and color degraded enhancement, the adaptive method of which is more close to the social organism learning process of high-level intelligent swarm, moreover, also can ensure the continuous evolution and strong stability of Swarm. The simulation results prove that AQPSO has the excellent performance in image enhancement algorithm.