APFlowNet: An inter-layer interpolation approach for soil CT images based on CNN and bidirectional optical flow

Computed tomography (CT) is an effective technique for characterizing the internal structure of soil. However, the voxels in CT images obtained by majority of medical scanners exhibit anisotropy, i.e., the resolution in the vertical direction is lower compared to the horizontal direction, which can have adverse effects on the characterization of soil morphological parameters and the quality of three-dimensional reconstructed images. Currently, existing interpolation methods for achieving voxel isotropy in soil CT images are unable to generate high -quality interpolation images at arbitrary positions between two slices, which leads to errors in the analysis of soil structure. Therefore, this study proposed an inter -layer interpolation method (APFlowNet) based on convolutional neural network (CNN) and bidirectional optical flow to generate high -quality images with isotropic voxels and assist in digital soil descriptions. The proposed method utilized an estimated image synthesis module to extract bidirectional optical flow between two input images and estimate optical flows from the input image to arbitrary interpolation positions, enabling the acquisition of overall continuous change. Subsequently, the intermediate image synthesis module was employed to extract the residual stream and its corresponding weights, facilitating the capture of detailed changes. Finally, the interpolation image synthesis module integrated the global and detail information to produce a high -precision interpolation image with isotropic voxels. Compared to the best -performing Linear method in traditional approaches, the APFlowNet method demonstrates superior performance with a peak signal-to-noise ratio (PSNR) of 32.637 dB and a structural similarity index (SSIM) of 0.928, representing improvements of 1.97% and 0.43%, respectively. This study showcased that the APFlowNet method not only increases the number of soil CT images but also achieves voxel isotropy, providing an intelligent technique for comprehending the internal structure of soil and multi -scale modeling.