Robust photometric stereo in a scattering medium via low-rank matrix completion and recovery

Photometric stereo is a popular method for its better detail recovery. However, when it is used in a scattering medium such as lake and ocean, the recovery result will be impacted negatively by the absorption, scattering and the impurities in water. In this paper, we present a new method to solve the problem of better 3D reconstruction via Low- Rank Matrix Completion and Recovery . First, we use the dark points like shadows and darkness in water to fit the scattering effect distribution and then remove the scattering from the image. Next, we use robust principal component analysis method (RPCA) to recover the image by removing the sparse noise including shadows, impurities and some corrupted points caused by backscatter compensation. Finally, we combine the RPCA results and least-squares (LS) results to get the normal and accomplish the 3D reconstruction. Extension experimental results demonstrate that our method achieves accurate estimates of surface normal and 3D reconstruction than previous techniques.