光学精密工程2017，Vol.25Issue(5)：1171-1177,7.DOI:10.3788/OPE.20172505.1171
基于低秩矩阵分解的光场稀疏采样及重构
Sparse sampling and reconstruction of compressive light field via low-rank matrix decomposition
摘要
Abstract
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.关键词
计算成像/光场成像/低秩稀疏分解/压缩采样/图像重构Key words
computational imaging/light field imaging/low rank and sparse decomposition/compressive sampling/images reconstruction分类
信息技术与安全科学引用本文复制引用
覃亚丽,张晓帅,余临倩..基于低秩矩阵分解的光场稀疏采样及重构[J].光学精密工程,2017,25(5):1171-1177,7.基金项目
国家自然科学基金资助项目(No.61675184,No.61275214,No.61405178,No.61205121) （No.61675184,No.61275214,No.61405178,No.61205121）
