Joint Spatial and Angular Super-Resolution from a Single Image
This solves the problem of generating detailed light fields from limited input for applications like depth estimation and refocusing, though it is incremental as it builds on existing super-resolution methods with novel losses.
The paper tackles the problem of jointly performing spatial and angular super-resolution from a single image to synthesize a dense, high-resolution light field, and the result shows that the proposed model outperforms state-of-the-art methods in quantitative and qualitative criteria.
Synthesizing a densely sampled light field from a single image is highly beneficial for many applications. Moreover, jointly solving both angular and spatial super-resolution problem also introduces new possibilities in light field imaging. The conventional method relies on physical-based rendering and a secondary network to solve the angular super-resolution problem. In addition, pixel-based loss limits the network capability to infer scene geometry globally. In this paper, we show that both super-resolution problems can be solved jointly from a single image by proposing a single end-to-end deep neural network that does not require a physical-based approach. Two novel loss functions based on known light field domain knowledge are proposed to enable the network to preserve the spatio-angular consistency between sub-aperture images. Experimental results show that the proposed model successfully synthesizes dense high resolution light field and it outperforms the state-of-the-art method in both quantitative and qualitative criteria. The method can be generalized to arbitrary scenes, rather than focusing on a particular subject. The synthesized light field can be used for various applications, such as depth estimation and refocusing.