3D medical images security via light-field imaging.

This Letter proposes a selective encryption scheme for three-dimensional (3D) medical images using light-field imaging and two-dimensional (2D) Moore cellular automata (MCA). We first utilize convolutional neural networks (CNNs) to obtain the saliency of each elemental image (EI) originating from a 3D medical image with different viewpoints, and successfully extract the region of interest (ROI) in each EI. In addition, we use 2D MCA with balanced rule to encrypt the ROI of each EI. Finally, the decrypted elemental image array (EIA) can be reconstructed into a full-color and full-parallax 3D image using the display device, which can be visually displayed to doctors so that they can observe from different angles to design accurate treatment plans and improve the level of medical treatment. Our work also requires no preprocessing of 3D images, which is more efficient than the method of using slices for encryption.

Deep-learning-based 3D object salient detection via light-field integral imaging.

This Letter proposes an effective light-field 3D saliency object detection (SOD) method, which is inspired by the idea that the spatial and angular information inherent in a light-field implicitly contains the geometry and reflection characteristics of the observed scene. These characteristics can provide effective background clues and depth information for 3D saliency reconstruction, which can greatly improve the accuracy of object detection and recognition. We use convolutional neural networks (CNNs) to detect the saliency of each elemental image (EI) with different viewpoints in an elemental image array (EIA) and the salient EIA is reconstructed by using a micro-lens array, forming a 3D salient map in the reconstructed space. Experimental results show that our method can generate high-quality 3D saliency maps and can be observed simultaneously from different angles and positions.