Cell-filtering-based multi-scale Shannon–Cosine wavelet denoising method for locust slice images

Shenghan Mei,Xiaochun Liu,Shuli Mei

doi:10.1142/s0219691319500358

Abstract

The locust slice images have all the features such as strong self-similarity, piecewise smoothness and nonlinear texture structure. Multi-scale interpolation operator is an effective tool to describe such structures, but it cannot overcome the influence of noise on images. Therefore, this research designed the Shannon–Cosine wavelet which possesses all the excellent properties such as interpolation, smoothness, compact support and normalization, then constructing multi-scale wavelet interpolative operator, the operator can be applied to decompose and reconstruct the images adaptively. Combining the operator with the local filter operator (mean and median), a multi-scale Shannon–Cosine wavelet denoising algorithm based on cell filtering is constructed in this research. The algorithm overcomes the disadvantages of multi-scale interpolation wavelet, which is only suitable for describing smooth signals, and realizes multi-scale noise reduction of locust slice images. The experimental results show that the proposed method can keep all kinds of texture structures in the slice image of locust. In the experiments, the locust slice images with mixture noise of Gaussian and salt–pepper are taken as examples to compare the performances of the proposed method and other typical denoising methods. The experimental results show that the Peak Signal-To-Noise Ratio (PSNR) of the denoised images obtained by the proposed method is greater 27.3%, 24.6%, 2.94%, 22.9% than Weiner filter, wavelet transform method, median and average filtering, respectively; and the Structural Similarity Index (SSIM) for measuring image quality is greater 31.1%, 31.3%, 15.5%, 10.2% than other four methods, respectively. As the variance of Gaussian white noise increases from 0.02 to 0.1, the values of PSNR and SSIM obtained by the proposed method only decrease by 11.94% and 13.33%, respectively, which are much less than other 4 methods. This shows that the proposed method possesses stronger adaptability.

Highlights

Locust slice images are usually applied to auxiliary analysis of the changes of the physiological structure of the infected locust in pesticide research fields.[13]
We review the properties of Shannon–Cosine wavelet first
The multi-scale interpolative operator is constructed with Shannon–Cosine wavelet (N = 23.123447719961405), and the cell filter is the median operator, in which each output pixel contains the median value in the 6 × 6 neighborhood around the corresponding pixel in the input image

Summary

Introduction

Locust slice images are usually applied to auxiliary analysis of the changes of the physiological structure of the infected locust in pesticide research fields.[13]. This is the reason why it is difficult to get excellent denoised images by the common denoising methods such as the nonlinear PDEs method,[16] shearlet transform,[2,8,10,12,17,18] and the coupling technology of the above two methods.[26] It is well known that the nonlinear PDEs method is an effective denoising tool for biomedical images

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Conclusion