Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
复制标题
基于阵列随机共振和改进鲸鱼优化算法的灰度图像去噪
DOI：
10.3390/app122312084
复制
发表时间：
2022-12-01
影响因子：
2.7
通讯作者：
Wang, Jing
中科院分区：
文献类型：
--
作者：
Huang, Weichao;Zhang, Ganggang;Wang, Jing
针对传统去噪算法对强噪声图像增强效果不佳的问题，提出了一种基于改进鲸鱼优化算法和参数自适应阵列随机共振的图像去噪算法。该算法通过降维扫描、编码、调制等处理，使含噪声的灰度图像成为适合于双稳随机共振模型的一维非周期二值脉冲调幅信号。然后，对传统的鲸鱼优化算法在初始解分布、全局搜索能力和种群多样性泛化等方面进行了改进。将改进的鲸鱼优化算法应用于随机共振参数的选择，有效地提高了阵列随机共振模型参数的自适应能力。最后，对随机共振输出进行解调、解码和反扫描，得到去噪图像。实验结果表明，与固定参数阵列随机共振法和经典图像去噪方法相比，本文算法在视觉效果和峰值信噪比指标上都有更好的表现，证明了该方法在图像去噪中的优势和有效应用。
Aiming at the poor effect of traditional denoising algorithms on image enhancement with strong noise, an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance is proposed in the paper. In this algorithm, through dimensionality reduction scanning, coding, modulation and other processing, the noise-containing gray image becomes a one-dimensional aperiodic binary pulse amplitude modulation signal suitable for a bistable stochastic resonance model. Then, the traditional whale optimization algorithm is improved in the initial solution distribution, global search ability and population diversity generalization. The improved whale optimization algorithm is applied to select the parameters of the stochastic resonance, which effectively improves the parameters self-adaptive of the array stochastic resonance model. Finally, the denoised image is obtained by demodulating, decoding and anti-scanning the stochastic resonance output. The experimental results show that compared with the array stochastic resonance method with fixed parameters and the classical image denoising method, the algorithm proposed in this paper has better performance in terms of visual effect and peak signal-to-noise ratio index, which proves the advantages and effective application of the method in image denoising.