摘要
为去噪的同时最大程度地保留蝗虫切片图像细小的边缘与纹理特征,利用Bandelet变换的多尺度特性和图像的几何特性,提出了基于Bandelet变换的参数化阈值函数的去噪算法。首先采用具有平移不变性的平稳小波对图像进行分解,然后利用Birge-Massart策略估计阈值、最小化Lagrange函数取得最佳几何流方向,并利用最小均方误差(MSE)原则优化四叉树,最后采用自适应Bayesshrink参数化阈值函数对图像进行去噪。实验结果表明,本文算法对图像边缘的稀疏表示效果比较理想,降噪后边缘更加清晰,纹理特征更多。对比其他方法,通过本文算法获得的峰值信噪比(PSNR)和结构相似度(SSIM)结果表明,其降噪性能显著提高。说明将基于Bandelet变换的参数化阈值函数算法应用于蝗虫切片图像去噪有效、可行,可为后续研究提供技术支持。
In order to better obtain the edge details and texture information of the image in the process of denoising,the balance was obtained in the removal and excessive smoothing of the image detail noise.A parameterize threshold function for denoising algorithm was proposed based on Bandelet transform,which took full advantage of the multi-scale characteristics of Bandelet transform and the geometric characteristics of image.The image was decomposed by using stationary wavelet with translation invariance to overcome the oscillation of the image and the threshold was estimated by Birge-Massart strategy.Then the optimal geometric flow direction was obtained by minimizing Lagrange function.The quadtree of Bandelet transform was optimized according to minimum mean square error (MSE) principle.Finally,the adaptive Bayesshrink parameterized threshold function was used to image denoising.The results showed that the proposed method performed more effectively to preserve the edge features and the fine structure of the denoising image.Compared with other methods,the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) obtained by the proposed algorithm showed that the performance of noise reduction was improved significantly.Therefore,the parameterized threshold function denoising algorithm based on Bandelet transform was feasible and effective in image denoising of locust slices,which provided technical support for its subsequent processing.
作者
王海华
张馨心
梅树立
WANG Haihua;ZHANG Xinxin;MEI Shuli(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2019年第B07期159-166,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(61871380)
北京市自然科学基金项目(4172034)
