2 research outputs found
Automatic Layer Separation using Light Field Imaging
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.Comment: 9 pages, 9 figure
Reflection Separation and Deblurring of Plenoptic Images
In this paper, we address the problem of reflection removal and deblurring
from a single image captured by a plenoptic camera. We develop a two-stage
approach to recover the scene depth and high resolution textures of the
reflected and transmitted layers. For depth estimation in the presence of
reflections, we train a classifier through convolutional neural networks. For
recovering high resolution textures, we assume that the scene is composed of
planar regions and perform the reconstruction of each layer by using an
explicit form of the plenoptic camera point spread function. The proposed
framework also recovers the sharp scene texture with different motion blurs
applied to each layer. We demonstrate our method on challenging real and
synthetic images.Comment: ACCV 201