On plenoptic sub-aperture view recovery

Light field imaging is recently made available to the mass market by Lytro and Raytrix commercial cameras. Thanks to a grid of microlenses put in front of the sensor, a plenoptic camera simultaneously captures several images of the scene under different viewing angles, providing an enormous advantage for post-capture applications, e.g., depth estimation and image refocusing. In this paper, we propose a fast framework to re-grid, denoise and up-sample the data of any plenoptic
camera. The proposed method relies on the prior sub-pixel estimation of micro-images centers and of inter-views disparities. Both objective and subjective experiments show the improved quality of our results in terms of preserving high frequencies and reducing noise and artifacts in low frequency content. Since the recovery of the pixels is independent of one another, the algorithm is highly parallelizable on GPU.

On plenoptic sub-aperture view recovery“, Mozhdeh Seifi, Neus Sabater, Valter Drazic, Patrick Pérez, 24th European Signal Processing Conference (EUSIPCO), 29 Aug.-2 Sept. 2016

Disparity-guided demosaicking of light field images

Light-field imaging has been recently introduced to mass market by the hand held plenoptic camera Lytro. Thanks to a microlens array placed between the main lens and the sensor, the captured data contains different views of the scene from different view points. This offers several post-capture applications, e.g., computationally changing the main lens focus. The raw data conversion in such cameras is however barely studied in the literature. The goal of this paper is to study the particularly overlooked problem of demosaicking the views for plenoptic cameras such as Lytro. We exploit the redundant sampling of scene content in the views, and show that disparities estimated from the mosaicked data can guide the demosaicking, resulting in minimum artifacts compared to the state of art methods. Besides, by properly addressing the view demultiplexing step, we take the first step towards light field super-resolution with negligible computational overload.

Disparity-guided demosaicking of light field images“, M. Seifi, N. Sabater, V. Drazic, P. Perez, IEEE International Conference on Image Processing (ICIP), 2014