Study of contrast variations with depth in focused plenoptic cameras.

A focused plenoptic camera has the ability to record and separate spatial and directional information of the incoming light. Combined with the appropriate algorithm, a 3D scene could be reconstructed from a single acquisition, over a depth range called plenoptic depth-of-field. In this Letter, we study the contrast variations with depth as a way to assess plenoptic depth-of-field. We take into account the impact of diffraction, defocus, and magnification on the resulting contrast. We measure the contrast directly on both simulated and acquired images. We demonstrate the importance of diffraction and magnification in the final contrast. Contrary to classical optics, the maximum of contrast is not centered around the main object plane, but around a shifted position, with a fast and nonsymmetric decrease of contrast.

Advanced light-field refocusing through tomographic modeling of the photographed scene.

Recently we have shown that light-field photography images can be interpreted as limited-angle cone-beam tomography acquisitions. Here, we use this property to develop a direct-space tomographic refocusing formulation that allows one to refocus both unfocused and focused light-field images. We express the reconstruction as a convex optimization problem, thus enabling the use of various regularization terms to help suppress artifacts, and a wide class of existing advanced tomographic algorithms. This formulation also supports super-resolved reconstructions and the correction of the optical system's limited frequency response (point spread function). We validate this method with numerical and real-world examples.

Tomographic approach for the quantitative scene reconstruction from light field images.

Current computational methods for light field photography model the ray-tracing geometry inside the plenoptic camera. This representation of the problem, and some common approximations, can lead to errors in the estimation of object sizes and positions. We propose a representation that leads to the correct reconstruction of object sizes and distances to the camera, by showing that light field images can be interpreted as limited angle cone-beam tomography acquisitions. We then quantitatively analyze its impact on image refocusing, depth estimation and volumetric reconstructions, comparing it against other possible representations. Finally, we validate these results with numerical and real-world examples.