Collection of light field and compression of data in light field imaging technology are urgent problems which need to be solved. In order to realize sparse sampling and restoration of the light field, a camera system to compress samplings based on low-rank structure of the light field was built for researching structural features of matrix of the light field and the reconstruction of light field images under compressive sampling. According to content similarities between each viewpoint image in static light field, those images were vectorized into a two-dimensional matrix by columns. The matrix presented a low-rank or approximated low-rank state. Low-rank decomposition of image matrix in the light field were finished, which shows that deflective low-rank parts emerge strong sparse properties, and low-rank and sparseness separately represented different data redundancies. Then, the camera sampling system fitted with the mask was measured through sparse random Noiselets conversion. Considering the reconstruction process was an optimization solution problem constrained by low-rank sparse correlation, the greedy iterative solution was adopted to separately reconstruct low-rank parts and sparse parts of light field matrix. The simulation result shows that the PSNR of reconstructed image that keeps disparity information among viewpoints of the light field maintains over 25 dB, thus meeting the requirement of sparse sampling for images of light field.