改进的MEA-WNN图像复原方法

Improved MEA-WNN image restoration method

模糊图像复原是计算机视觉和图像处理领域的重要任务。针对思维进化算法(mind evolutionary algorithm, MEA)和小波神经网络(wavelet neural network, WNN)相结合的图像复原模型中,MEA的得分函数相对差别小、选优功能较弱等问题,提出了一种改进的MEA-WNN图像复原方法。该方法采用逻辑回归函数进行幂律变换,增加得分之间的差别,从而增强MEA的选优功能。将改进的模型与传统的基于WNN和MEA-WNN的图像复原模型进行对比,改进的模型把复原图像峰值信噪比(peak signal-to-noise ratio,PSNR)分别提高15%和6.5%、结构相似性(structural similarity,SSIM)提高了6.1%和5%,实验结果证明改进模型的有效性和优越性。

Blurred image restoration is an important task in the field of computer vision and image processing.In view of the problem of score function has relatively small differences and weak optimization function of mind evolutionary algorithm(MEA)in image restoration based on combination of MEA and wavelet neural network(WNN),an improved MEA-WNN image restoration model is proposed.The difference between score function is increased by using power law transformation and logistic regression function,therefore,the selecting function of MEA is enhanced significantly.Comparative experiments are conducted between improved and traditional WNN and MEA-WNN based image restoration model,compared to WNN and MEA-WNN based models,the improved model can increase the peak signal-to-noise ratio(PSNR)by 15%and 6.5%,and structural similarity(SSIM)by 6.1%and 5%respectively.Effectiveness and superiority of improved model is proved by some experimental results.