Adaptive compressed 3D ghost imaging based on the variation of surface normals

Three-dimensional (3D) imaging can be reconstructed by a computational ghost imaging system with single pixel detectors based on a photometric stereo, but the requirement of large measurements and long imaging times are obstacles to its development. Also, the compressibility of the target’s surface normals has not been fully studied, which causes the waste in sampling efficiency in single-pixel imaging. In this paper, we propose a method to adaptively measure the object’s 3D information based on surface normals. In the proposed method, the regions of object’s surface are illuminated by patterns of different spatial resolutions according to the variation of surface normals. The experimental results demonstrate that our proposed scheme can reduce measurements and preserve the quality of the formed 3D image.