Three-Dimensional (3D) Visualization under Extremely Low Light Conditions Using Kalman Filter

In recent years, research on three-dimensional (3D) reconstruction under low illumination environment has been reported. Photon-counting integral imaging is one of the techniques for visualizing 3D images under low light conditions. However, conventional photon-counting integral imaging has the problem that results are random because Poisson random numbers are temporally and spatially independent. Therefore, in this paper, we apply a technique called Kalman filter to photon-counting integral imaging, which corrects data groups with errors, to improve the visual quality of results. The purpose of this paper is to reduce randomness and improve the accuracy of visualization for results by incorporating the Kalman filter into 3D reconstruction images under extremely low light conditions. Since the proposed method has better structure similarity (SSIM), peak signal-to-noise ratio (PSNR) and cross-correlation values than the conventional method, it can be said that the visualization of low illuminated images can be accurate. In addition, the proposed method is expected to accelerate the development of autonomous driving technology and security camera technology.

Three-Dimensional Digital Zooming of Integral Imaging under Photon-Starved Conditions

In this paper, we propose new three-dimensional (3D) visualization of objects at long distance under photon-starved conditions. In conventional three-dimensional image visualization techniques, the visual quality of three-dimensional images may be degraded because object images at long distances may have low resolution. Thus, in our proposed method, we utilize digital zooming, which can crop and interpolate the region of interest from the image to improve the visual quality of three-dimensional images at long distances. Under photon-starved conditions, three-dimensional images at long distances may not be visualized due to the lack of the number of photons. Photon counting integral imaging can be used to solve this problem, but objects at long distance may still have a small number of photons. In our method, a three-dimensional image can be reconstructed, since photon counting integral imaging with digital zooming is used. In addition, to estimate a more accurate three-dimensional image at long distance under photon-starved conditions, in this paper, multiple observation photon counting integral imaging (i.e., N observation photon counting integral imaging) is used. To show the feasibility of our proposed method, we implement the optical experiments and calculate performance metrics, such as peak sidelobe ratio. Therefore, our method can improve the visualization of three-dimensional objects at long distances under photon-starved conditions.

Three-Dimensional Integral Imaging with Enhanced Lateral and Longitudinal Resolutions Using Multiple Pickup Positions

In this paper, we propose an enhancement of three-dimensional (3D) image visualization techniques by using different pickup plane reconstructions. In conventional 3D visualization techniques, synthetic aperture integral imaging (SAII) and volumetric computational reconstruction (VCR) can be utilized. However, due to the lack of image information and shifting pixels, it may be difficult to obtain better lateral and longitudinal resolutions of 3D images. Thus, we propose a new elemental image acquisition and computational reconstruction to improve both the lateral and longitudinal resolutions of 3D objects. To prove the feasibility of our proposed method, we present the performance metrics, such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and peak-to-sidelobe ratio (PSR). Therefore, our method can improve both the lateral and longitudinal resolutions of 3D objects more than the conventional technique.