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

View Fullscreen

Accurate Disparity Estimation for Plenoptic Images

In this paper we propose a post-processing pipeline to recover accurately the views (light-field) from the raw data of a plenoptic camera such as Lytro and to estimate disparity maps in a novel way from such a light-field. First, the microlens centers are estimated and then the raw image is demultiplexed without demosaicking it beforehand. Then, we present a new block-matching algorithm to estimate disparities for the mosaicked plenoptic views. Our algorithm exploits at best the configuration given by the plenoptic camera: (i) the views are horizontally and vertically rectified and have the same baseline, and therefore (ii) at each point, the vertical and horizontal disparities are the same. Our strategy of demultiplexing without demosaicking avoids image artifacts due to view cross-talk and helps estimating more accurate disparity maps. Finally, we compare our results with state-of-the-art methods.

Accurate Disparity Estimation for Plenoptic Images“, N. Sabater, M. Seifi, V. Drazic, G. Sandri, P. Perez, European Conference on Computer Vision (ECCV) 2014 Workshops.

View Fullscreen