Optimization of Compressive Light Field Display in Dual-Guided Learning

Glass-free compressive light field (CLF) display gains much attention due to their compatibility in holographic-like and three-dimensional (3D) demonstration. Opposite to other analogous devices, CLF display can provide binocular and motion parallaxes by stacking multiple liquid crystal screens without any extra accessories. It is possible to bring the immersive and accommodative experience upon a well-pleasing visual consequence. Conventionally, the excessive processing time impacts its practical value in commercial, along with the severe degradation of display brightness. Therefore, in this paper, we propose a learning-based factorization framework to promote the visual results and expedite the layer decomposition and display adaption. It utilizes the advantage of a dual-guided system and residual learning to implement pixel-wise information extraction and refinement. The experimental results illustrate the outperformance of our proposed method over the conventional iterative factorization. Furthermore, a three-layered CLF prototype has been assembled to verify the practicality of our method.