Reconstruction of Compressively Sampled Ray Space by Using DCT Basis and Statistically-Weighted L1 Norm Optimization

In recent years, ray space (or light field in other literatures) photography has gained a great popularity in the area of computer vision and image processing, and an efficient acquisition of a ray space is of great significance in the practical application. In order to handle the huge data problem in the acquisition process, in this paper, we propose a method of compressively sampling and reconstructing one ray space. In our method, one weighted matrix which reflects the amplitude structure of non-zero coefficients in 2D-DCT domain is designed and generated by using statistics from available data set. The weighted matrix is integrated in l(1) norm optimization to reconstruct the ray space, and we name this method as statistically-weighted l(1) norm optimization. Experimental result shows that the proposed method achieves better reconstruction result at both low (0.1 of original sampling rate) and high (0.5 of original sampling rate) subsampling rates. In addition, the reconstruction time is also reduced by 25% compared to the reconstruction time by plain l(1) norm optimization.