Registration and inpainting of biological slice images based on Bendlet transform and interval Shannon–Cosine wavelet

Abstract

The fold that appeared in the micro-slice images needs to be inpainted exactly so that it can meet the requirements of the scientific experiments. The biological slice image usually consists of piecewise smooth regions with the closed loop contour which can be represented by the Bendlet function proposed in recent years. Therefore, a novel image inpainting method based on Bendlet and interval Shannon–Cosine wavelet is proposed. Since the deformation of the locust slice image is flexible, it is necessary to obtain as many feature points as possible to ensure the accuracy of the inpainting, so we introduce the curvature as a new registration element for registration. First, the homography matrix is obtained by calculating the correct feature points by our proposed registration method. Second, the fold position is located by homography matrix and inpainted by Shannon–Cosine interval wavelet interpolation. Finally, the pixel difference is eliminated through adaptive fusion. The results indicate that, in comparison to the SURF and ORB algorithms, our registration method significantly enhances the extraction of feature points, achieving a more even distribution. Furthermore, when compared to four other methods (K-SVD, BSCB, TV and Criminisi), as well as various interpolation methods such as cubic polynomial interpolation, cubic spline interpolation, and nearest neighbor interpolation, our approach consistently achieves the highest PSNR and SSIM values.