Longitudinal resolution of three-dimensional integral imaging in the presence of noise.

The two-point source longitudinal resolution of three-dimensional integral imaging depends on several factors including the number of sensors, sensor pixel size, pitch between sensors, and the lens point spread function. We assume the two-point sources to be resolved if their point spread functions can be resolved in any one of the sensors. Previous studies of integral imaging longitudinal resolution either rely on geometrical optics formulation or assume the point spread function to be of sub-pixel size, thus neglecting the effect of the lens.

3D object tracking using integral imaging with mutual information and Bayesian optimization.

Integral imaging has proven useful for three-dimensional (3D) object visualization in adverse environmental conditions such as partial occlusion and low light. This paper considers the problem of 3D object tracking. Two-dimensional (2D) object tracking within a scene is an active research area.

Underwater object detection and temporal signal detection in turbid water using 3D-integral imaging and deep learning.

Underwater scattering caused by suspended particles in the water severely degrades signal detection performance and poses significant challenges to the problem of object detection. This paper introduces an integrated dual-function deep learning-based underwater object detection and classification and temporal signal detection algorithm using three-dimensional (3D) integral imaging (InIm) under degraded conditions. The proposed system is an efficient object classification and temporal signal detection system for degraded environments such as turbidity and partial occlusion and also provides the object range in the scene.

Generalization of the two-point-source resolution criterion in the presence of noise.

The two-point-source resolution criterion is widely used to quantify the performance of imaging systems. The two main approaches for the computation of the two-point-source resolution are the detection theoretic and visual analyses. The first assumes a shift-invariant system and lacks the ability to incorporate two different point spread functions (PSFs), which may be required in certain situations like computing axial resolution.

3D object detection through fog and occlusion: passive integral imaging vs active (LiDAR) sensing.

In this paper, we address the problem of object recognition in degraded environments including fog and partial occlusion. Both long wave infrared (LWIR) imaging systems and LiDAR (time of flight) imaging systems using Azure Kinect, which combine conventional visible and lidar sensing information, have been previously demonstrated for object recognition in ideal conditions. However, the object detection performance of Azure Kinect depth imaging systems may decrease significantly in adverse weather conditions such as fog, rain, and snow.

Lowlight object recognition by deep learning with passive three-dimensional integral imaging in visible and long wave infrared wavelengths.

Traditionally, long wave infrared imaging has been used in photon starved conditions for object detection and classification. We investigate passive three-dimensional (3D) integral imaging (InIm) in visible spectrum for object classification using deep neural networks in photon-starved conditions and under partial occlusion. We compare the proposed passive 3D InIm operating in the visible domain with that of the long wave infrared sensing in both 2D and 3D imaging cases for object classification in degraded conditions.

Three-dimensional polarimetric image restoration in low light with deep residual learning and integral imaging.

Polarimetric imaging can become challenging in degraded environments such as low light illumination conditions or in partial occlusions. In this paper, we propose the denoising convolutional neural network (DnCNN) model with three-dimensional (3D) integral imaging to enhance the reconstructed image quality of polarimetric imaging in degraded environments such as low light and partial occlusions. The DnCNN is trained based on the physical model of the image capture in degraded environments to enhance the visualization of polarimetric imaging where simulated low light polarimetric images are used in the training process.

Deep learning polarimetric three-dimensional integral imaging object recognition in adverse environmental conditions.

Polarimetric imaging is useful for object recognition and material classification because of its ability to discriminate objects based on polarimetric signatures of materials. Polarimetric imaging of an object captures important physical properties such as shape and surface properties and can be effective even in low light environments. Integral imaging is a passive three-dimensional (3D) imaging approach that takes advantage of multiple 2D imaging perspectives to perform 3D reconstruction.

Three-dimensional polarimetric integral imaging in photon-starved conditions: performance comparison between visible and long wave infrared imaging.

Three-dimensional (3D) polarimetric integral imaging (InIm) to extract the 3D polarimetric information of objects in photon-starved conditions is investigated using a low noise visible range camera and a long wave infrared (LWIR) range camera, and the performance between the two sensors is compared. Stokes polarization parameters and degree of polarization (DoP) are calculated to extract the polarimetric information of the 3D scene while integral imaging reconstruction provides depth information and improves the performance of low-light imaging tasks. An LWIR wire grid polarizer and a linear polarizer film are used as polarimetric objects for the LWIR range and visible range cameras, respectively.

Relationship between the source size at the diffuser plane and the longitudinal spatial coherence function of the optical coherence microscopy system.

Coherence properties of light sources are indispensable for optical coherence microscopy/tomography as they greatly influence the signal-to-noise ratio, axial resolution, and penetration depth of the system. In the present paper, we report the investigation of longitudinal spatial coherence properties of a pseudothermal light source (PTS) as a function of the laser spot size at the rotating diffuser plate. The laser spot size is varied by translating a microscope objective lens toward or away from the diffuser plate.