The biological cross-sectional images majorly consist of closed-loop structures, which are suitable to be represented by the second-order shearlet system with curvature (Bendlet). In this study, an adaptive filter method for preserving textures in the bendlet domain is proposed. The Bendlet system represents the original image as an image feature database based on image size and Bendlet parameters. This database can be divided into image high-frequency and low-frequency sub-bands separately. The low-frequency sub-bands adequately represent the closed-loop structure of the cross-sectional images and the high-frequency sub-bands accurately represent the detailed textural features of the images, which reflect the characteristics of Bendlet and can be effectively distinguished from the Shearlet system. The proposed method takes full advantage of this feature, then selects the appropriate thresholds based on the images' texture distribution characteristics in the database to eliminate noise. The locust slice images are taken as an example to test the proposed method. The experimental results show that the proposed method can significantly eliminate the low-level Gaussian noise and protect the image information compared with other popular denoising algorithms. The PSNR and SSIM obtained are better than other methods. The proposed algorithm can be effectively applied to other biological cross-sectional images.
Keywords: Bendlet; Shearlet; adaptive filter method; biological slice images; image denoising.