High Dynamic Range Light Fields via Weighted Low Rank Approximation

Abstract

In this paper, we propose a method for capturing High Dynamic Range (HDR) light fields with dense viewpoint sampling. Analogously to the traditional HDR acquisition process, several light fields are captured at varying exposures with a plenoptic camera. The RAW data is de-multiplexed to retrieve all light field viewpoints for each exposure and perform a soft detection of saturated pixels. Considering a matrix which concatenates all the vectorized views, we formulate the problem of recovering saturated areas as a Weighted Low Rank Approximation (WLRA) where the weights are defined from the soft saturation detection. We show that our algorithm successfully recovers the parallax in the over-exposed areas while the Truncated Nuclear Norm (TNN) minimization, traditionally used for single view HDR imaging, does not generalize to light fields. Advantages of our weighted approach as well as the simultaneous processing of all the viewpoints are also demonstrated in our experiments.

Poster

The poster presented at ICIP 2018 can be downloaded here

Related publications

Mikael Le Pendu, Christine Guillemot and Aljosa Smolic, “High Dynamic Range Light Fields via Weighted Low Rank Approximation”, ICIP 2018.

Mikael Le Pendu, Xiaoran Jiang and Christine Guillemot, “Light Field inpainting via Low Rank Matrix completion”, IEEE Transactions on Image Processing, vol. 27, No. 4, pp. 1981-1993, Jan. 2018.