A novel light field super-resolution framework based on hybrid imaging system

We propose a novel light field super-resolution framework based on hybrid imaging system, which combines two different imaging mechanisms: conventional imaging and current art-of-the-state imaging - light field imaging. We take advantage of conventional imaging in spatial resolution to make up light field and reconstruct a higher quality light field. In our method, we classify the points of the 3D scene: First, for highlight and occlusion, dictionary learning based interpolation is utilized, Second, for other areas, an improved patch matching algorithm is applied. As shown in experimental results, compared with four methods, which include the art-of-the-state algorithms, our approach is effective.