Computational Large Field-of-View RGB-D Integral Imaging System

The integral imaging system has received considerable research attention because it can be applied to real-time three-dimensional image displays with a continuous view angle without supplementary devices. Most previous approaches place a physical micro-lens array in front of the image, where each lens looks different depending on the viewing angle. A computational integral imaging system with a virtual micro-lens arrays has been proposed in order to provide flexibility for users to change micro-lens arrays and focal length while reducing distortions due to physical mismatches with the lens arrays. However, computational integral imaging methods only represent part of the whole image because the size of virtual lens arrays is much smaller than the given large-scale images when dealing with large-scale images. As a result, the previous approaches produce sub-aperture images with a small field of view and need additional devices for depth information to apply to integral imaging pickup systems. In this paper, we present a single image-based computational RGB-D integral imaging pickup system for a large field of view in real time. The proposed system comprises three steps: deep learning-based automatic depth map estimation from an RGB input image without the help of an additional device, a hierarchical integral imaging system for a large field of view in real time, and post-processing for optimized visualization of the failed pickup area using an inpainting method. Quantitative and qualitative experimental results verify the proposed approach’s robustness.

Image Enhancement for Computational Integral Imaging Reconstruction via Four-Dimensional Image Structure

This paper describes the image enhancement of a computational integral imaging reconstruction method via reconstructing a four-dimensional (4-D) image structure. A computational reconstruction method for high-resolution three-dimensional (3-D) images is highly required in 3-D applications such as 3-D visualization and 3-D object recognition. To improve the visual quality of reconstructed images, we introduce an adjustable parameter to produce a group of 3-D images from a single elemental image array. The adjustable parameter controls overlapping in back projection with a transformation of cropping and translating elemental images. It turns out that the new parameter is an independent parameter from the reconstruction position to reconstruct a 4-D image structure with four axes of x, y, z, and k. The 4-D image structure of the proposed method provides more visual information than existing methods. Computer simulations and optical experiments are carried out to show the feasibility of the proposed method. The results indicate that our method enhances the image quality of 3-D images by providing a 4-D image structure with the adjustable parameter.

Depth extraction in computational integral imaging based on bilinear interpolation

We proposed a method using a merit function to determine the depth of objects in computational integral imaging by analyzing the existing methods for depth extraction of target objects. To improve the resolution of reconstructed slice images, we use a digital camera moving in horizontal and vertical direction with the set interval to get elemental images with high resolution and bilinear interpolation algorithm to increase the number of pixels in slice image which improves the resolution obviously. To show the feasibility of the proposed method, we carried out our experiment and presented the results. We also compared it with other merit functions. The results show that merit function SMD2 to determine the depth of objects is more accurate and suitable for real-time application.

An Improved Security 3D Watermarking Method Using Computational Integral Imaging Cryptosystem

Robustness and security are difficult to be solved by conventional two-dimensional (2D) digital watermarking technology. In recent years, three-dimensional (3D) digital watermarking has become a new hotspot in optical information security. This paper presents a new improved security 3D digital watermarking method based on computational integrated imaging cryptosystem. Firstly, 3D digital watermarking is generated and encrypted by computational integral imaging cryptosystem that is implemented with smart pseudoscopic-to-orthoscopic conversion (SPOC) model. Secondly, discrete wavelet transform algorithm is applied to embed and extract the 3D digital watermarking. Finally, the extracted watermark is decrypted, and3D digital watermarking is displayed by integral imaging system. The feasibility and effectiveness of the proposed method is demonstrated by experiment. A primary implication of encrypted processing is that the majority of integral imaging cryptosystem will be encryption-in-the-loop applications, and the majority of system will improve the security and robustness of 3D digital watermarking. The new method is able to meet the requirements of robustness and security. Image quality and display quality achieve these criterions of the human visual model. The proposed method can be applied in the aspects of cloud computing and big data.