Simultaneous Color Restoration and Depth Estimation in Light Field Imaging

Abstract

Recent studies in the light field imaging have shown the potential and advantages of different light field information processes. In most of the existing techniques, the processing pipeline of light field has been treated in a step-by-step manner, and each step is considered to be independent from the others. For example, in light field color demosaicing, inferring the scene geometry is treated as an irrelevant and negligible task, and vice versa. Such processing techniques may fail due to the inherent connection among different steps, and result in both corrupted post-processing and defective pre-processing results. In this paper, we address the interaction between color interpolation and depth estimation in light field, and propose a probabilistic approach to handle these two processing steps jointly. This probabilistic framework is based on a Markov Random Fields —Collaborative Graph Model for simultaneous Demosaicing and Depth Estimation (CGMDD)—to explore the color-depth interdependence from general light field sampling. Experimental results show that both image interpolation quality and depth estimation can benefit from their interaction, mainly for processes such as image demosaicing which are shown to be sensitive to depth information, especially for light field sampling with large baselines.

Keywords

• Light field
• demosaicing
• depth estimation
• Markov random field
• graph model