Applications Research of Improved Genetic Algorithm in Image Denoising????(Open Access)

作者：Chen, Ming

来源：1st International Conference on Advanced Algorithms and Control Engineering, ICAACE 2018, 2018-08-10 To 2018-08-12.

DOI：10.1088/1742-6596/1087/2/022032

• Faster convergence
• Image noise
• Image signal
• Quality evaluation models
• Relative errors
• Slow convergences
• Traditional genetic algorithms
• Visual effects

In the process of generation, transmission and recording, image signals are often disturbed by various noises, which seriously affect the visual effect of images. Noise is the main factor affecting image quality, which has a great influence on the subsequent processing of images. Therefore, it is of great practical significance to adopt appropriate methods to reduce noise. In this paper, several common methods of image noise removal are introduced, and the denoising quality evaluation model is given. The traditional genetic algorithm has the characteristics of slow convergence and big relevant error in image denoising. We improved the genetic algorithm and applied it into image denoising. Experimental results show that the improved genetic algorithm has faster convergence speed and smaller relative error, which improves the efficiency and quality of image denoising.

• 出版日期2018
• 单位三峡大学