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 ι1 norm optimization to
reconstruct the ray space, and we name this method as statistically-weighted ι1 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 ι1 norm optimization.
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