Elena Garces

We present a method to automatically decompose a light field into its intrinsic shading and albedo components. Contrary to previous work targeted to two‐dimensional (2D) single images and videos, a light field is a 4D structure that captures non‐integrated incoming radiance over a discrete angular domain. This higher dimensionality of the problem renders previous state‐of‐the‐art algorithms impractical either due to their cost of processing a single 2D slice, or their inability to enforce proper coherence in additional dimensions. We propose a new decomposition algorithm that jointly optimizes the whole light field data for proper angular coherence. For efficiency, we extend Retinex theory, working on the gradient domain, where new albedo and occlusion terms are introduced. Results show that our method provides 4D intrinsic decompositions difficult to achieve with previous state‐of‐the‐art algorithms. We further provide a comprehensive analysis and comparisons with existing intrinsic image/video decomposition methods on light field images.

Many high‐level image processing tasks require an estimate of the positions, directions and relative intensities of the light sources that illuminated the depicted scene. In image‐based rendering, augmented reality and computer vision, such tasks include matching image contents based on illumination, inserting rendered synthetic objects into a natural image, intrinsic images, shape from shading and image relighting. Yet, accurate and robust illumination estimation, particularly from a single image, is a highly ill‐posed problem. In this paper, we present a new method to estimate the illumination in a single image as a combination of achromatic lights with their 3D directions and relative intensities. In contrast to previous methods, we base our azimuth angle estimation on curve fitting and recursive refinement of the number of light sources. Similarly, we present a novel surface normal approximation using an osculating arc for the estimation of zenith angles. By means of a new data set of ground‐truth data and images, we demonstrate that our approach produces more robust and accurate results, and show its versatility through novel applications such as image compositing and analysis.