Automatic Layer Separation using Light Field Imaging
This addresses the problem of layer separation in imaging for applications like computer vision or photography, but it appears incremental as it builds on existing RPCA and regularization methods.
The paper tackles the problem of automatically separating reflection or translucent layers from a scene while estimating depth, using light field imaging and minimizing rank via RPCA with regularization. Experimental results on synthetic and real data demonstrate the technique's robustness and reliability across a broad range of layer separation problems.
We propose a novel approach that jointly removes reflection or translucent layer from a scene and estimates scene depth. The input data are captured via light field imaging. The problem is couched as minimizing the rank of the transmitted scene layer via Robust Principle Component Analysis (RPCA). We also impose regularization based on piecewise smoothness, gradient sparsity, and layer independence to simultaneously recover 3D geometry of the transmitted layer. Experimental results on synthetic and real data show that our technique is robust and reliable, and can handle a broad range of layer separation problems.