Affiliation:
1. School of Professional Practice and Leadership, University of Technology Sydney, Ultimo, NSW 2007, Australia
Abstract
Light field reconstruction and synthesis algorithms are essential for improving the lower spatial resolution for hand-held plenoptic cameras. Previous light field synthesis algorithms produce blurred regions around depth discontinuities, especially for stereo-based algorithms, where no information is available to fill the occluded areas in the light field image. In this paper, we propose a light field synthesis algorithm that uses the focal stack images and the all-in-focus image to synthesize a 9 × 9 sub-aperture view light field image. Our approach uses depth from defocus to estimate a depth map. Then, we use the depth map and the all-in-focus image to synthesize the sub-aperture views, and their corresponding depth maps by mimicking the apparent shifting of the central image according to the depth values. We handle the occluded regions in the synthesized sub-aperture views by filling them with the information recovered from the focal stack images. We also show that, if the depth levels in the image are known, we can synthesize a high-accuracy light field image with just five focal stack images. The accuracy of our approach is compared with three state-of-the-art algorithms: one non-learning and two CNN-based approaches, and the results show that our algorithm outperforms all three in terms of PSNR and SSIM metrics.
Funder
Australian Government Research Training Program
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference40 articles.
1. Ng, R. (2006). Digital Light Field Photography, Stanford University California.
2. Light field image processing: An overview;Wu;IEEE J. Sel. Top. Signal Process.,2017
3. High performance imaging using large camera arrays;Wilburn;Proceedings of the ACM Transactions on Graphics (TOG),2005
4. Light field photography with a hand-held plenoptic camera;Ng;Comput. Sci. Tech. Rep. CSTR,2005
5. Sharma, R., Perry, S., and Cheng, E. (2022). Noise-Resilient Depth Estimation for Light Field Images Using Focal Stack and FFT Analysis. Sensors, 22.