Research On Dense Reconstruction Method Of Sparse Light Field Based On Optical Flow Constraint Equation

The Light-field can record the characteristics of high-dimensional light data in the real environment to perceive the changes in the surrounding dynamic environment to play the light field's role in environmental decision-making in an intelligent system.Light field imaging technology breaks through classical imaging models and digital camera limitations.The expression of light field all-optical function defines the relationship of light in multiple dimensions such as time domain,space domain,and spectrum.It then completes the tasks of intelligent processing,such as coupling perception and decoupling reconstruction.At the same time,it enables the light field to image multi-dimensional and large-scale dynamic scenes.Because of its unique advantages,light field imaging technology has been widely used in the areas of Refocus,virtual reality,life science,and national security.However,with the discrete sampling of high-dimensional data,light field imaging technology is facing the fundamental problem of obtaining high spatial resolution and high angle resolution light field simultaneously.How to carry out high-quality,dense reconstruction of sparse angle dimension light field data has become an urgent problem to be solved.Aiming at the problem that light field imaging technology cannot obtain high spatial resolution and high angular resolution light field simultaneously,this paper researches the optical flow field's sparse light field density reconstruction algorithm.Starting from the basic principle of two-dimensional optical flow and based on the biplane model of the light field,this paper proposes a new mathematical model of a four-dimensional optical flow constraint equation,which is used to determine the position coordinates of the new viewing angle.Through interpolation calculation and image inversion,the intensity value of new coordinates is obtained point by point,and finally,a high-quality new perspective synthetic image is obtained.Experiments in several different scenarios verify that the proposed algorithm has strong adaptability.At the same time,the quantitative evaluation results show that the algorithm can complete the task of sparse light field dense reconstruction in different scenes.