High-quality 3D display system for an integral imaging microscope using a simplified direction-inversed computation based on user interaction

High-Quality 3D Visualization System for Light-Field Microscopy with Fine-Scale Shape Measurement through Accurate 3D Surface Data

We propose a light-field microscopy display system that provides improved image quality and realistic three-dimensional (3D) measurement information. Our approach acquires both high-resolution two-dimensional (2D) and light-field images of the specimen sequentially. We put forward a matting Laplacian-based depth estimation algorithm to obtain nearly realistic 3D surface data, allowing the calculation of depth data, which is relatively close to the actual surface, and measurement information from the light-field images of specimens. High-reliability area data of the focus measure map and spatial affinity information of the matting Laplacian are used to estimate nearly realistic depths. This process represents a reference value for the light-field microscopy depth range that was not previously available. A 3D model is regenerated by combining the depth data and the high-resolution 2D image. The element image array is rendered through a simplified direction-reversal calculation method, which depends on user interaction from the 3D model and is displayed on the 3D display device. We confirm that the proposed system increases the accuracy of depth estimation and measurement and improves the quality of visualization and 3D display images.

Light-field 3D encryption based on the monocular depth rendering

The real-time performance of light-field 3D encryption technology based on the integral imaging principle is restricted by the acquisition speed and the data of the elemental image array (EIA). Herein, we propose a light-field 3D encryption scheme based on monocular depth rendering. With the help of a convolution residuals network (CRN), the proposed scheme can generate the corresponding depth map from a single RGB image and simplify the pickup process of the EIA according to the image mapping. For encryption, using reversible state loop cellular automata (RSL-CA) to encrypt a single RGB image updates traditional 3D encryption, greatly improving the security and efficiency of the encryption algorithm. It is experimentally demonstrated that optical 3D reconstruction is clear and brightly colorful and also has a good parallax effect. The proposed method can open a brand-new research perspective for light-field 3D encryption.

Depth Estimation for Integral Imaging Microscopy Using a 3D-2D CNN with a Weighted Median Filter

This study proposes a robust depth map framework based on a convolutional neural network (CNN) to calculate disparities using multi-direction epipolar plane images (EPIs). A combination of three-dimensional (3D) and two-dimensional (2D) CNN-based deep learning networks is used to extract the features from each input stream separately. The 3D convolutional blocks are adapted according to the disparity of different directions of epipolar images, and 2D-CNNs are employed to minimize data loss. Finally, the multi-stream networks are merged to restore the depth information. A fully convolutional approach is scalable, which can handle any size of input and is less prone to overfitting. However, there is some noise in the direction of the edge. A weighted median filtering (WMF) is used to acquire the boundary information and improve the accuracy of the results to overcome this issue. Experimental results indicate that the suggested deep learning network architecture outperforms other architectures in terms of depth estimation accuracy.