Light Field Super resolution: A Bayesian Approach
We present our prototype light field camera system, and demonstrate
its application to reconstructing high-resolution images and depth
maps of a scene from a single snapshot.
These types of cameras sample the light field of a scene by trading
off spatial resolution with angular resolution. Current methods to
process the resulting light field produce images at a resolution that
is much lower than that of traditional imaging devices.
However, by explicitly modeling the image formation process and
incorporating priors such as Lambertianity and texture
statistics, these types of images can be reconstructed at a higher
resolution. We formulate a solution under the Bayesian framework to
reconstruct both the depth map of the scene, and the super resolved
radiance image. We demonstrate the method on both synthetic and real
images captured with our light-field camera prototype.