Overcomplete compressed sensing of ray space for generating free viewpoint images
Free Viewpoint Image (FVI) technique has gained a great popularity because it enables people to freely choose the viewpoints from which they watch the targets and scenes. Generation of FVI requires all the information of a 3-D virtual space. Ray space is a direct representation of complete 3-D information, and FVIs can be generated simply by cutting ray space. However, in the straightforward construction of a ray space, a numerous number of images have to be captured in advance which triggered data explosive from data acquisiton to data storage and transmission. In this paper, we focus on compressed sensing to sparsely capture a ray space at encoder and reconstruct it at decoder. Specifically, we propose to adopt overcomplete dictionaries which are produced by learning methods, to sparsely represent the ray space data. Experimental results show that the proposed dictionary is much better than the structured dictionary in previous work or orthogonal basis in terms of the reconstruction quality of the ray space from compressively sensed data.