改进的MEA-WNN图像复原方法
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作者单位:

(1.新疆师范大学 数学科学学院,新疆 乌鲁木齐 830017; 2.新疆心智发展与学习科学重点实验室,新疆 乌鲁木齐 830017)

作者简介:

姑丽加玛丽·麦麦提艾力 (1984-),女,博士,副教授,硕士生导师,主要从事人工智能、语音和图像处理方面的研究 。

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基金项目:

国家自然科学基金(61462087,6175316)和新疆自然科学特培项目(022D03029)资助项目


Improved MEA-WNN image restoration method
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Affiliation:

(1.School of Mathematical Science, Xinjiang Normal University, Urumqi, Xinjiang 830017, China;2.Xinjiang Key Laboratory of Mental Development and Learning Science, Urumqi, Xinjiang 830017, China)

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    摘要:

    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。

    Abstract:

    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,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.

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古兰拜尔·肉孜,姑丽加玛丽·麦麦提艾力.改进的MEA-WNN图像复原方法[J].光电子激光,2024,35(8):803~809

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  • 收稿日期:2022-12-29
  • 最后修改日期:2023-03-20
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  • 在线发布日期: 2024-07-05
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