We present a method to automatically decompose a light field into its intrinsic shading and albedo components. Contrary to previous work targeted to 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 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.
* Light fields. Related work on intrinsic decomposition of light field images and videos has been published concurrently. @cite_45 present a general approach for stabilizing the results of image processing algorithms over an array of images and videos. Their approach can produce very stable results, but its generality does not exploit a 4D structure that can be used to handle complex non-lambertian materials @cite_6 @cite_15 . On the other hand, Alperovich and Goldluecke @cite_41 present an approach similar to ours posing the problem in ray space. By doing this, they ensure angular coherence and also handle non-lambertian materials. While we do not handle such materials explicitly, our algorithm produces sharper and more stable results, with comparable reconstructions of reflectances under specular highlights.
@cite_41 @cite_15 @cite_45 @cite_6
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We present a novel variational model for intrinsic light field decomposition, which is performed on four-dimensional ray space instead of a traditional 2D image. As most existing intrinsic image algorithms are designed for Lambertian objects, their performance suffers when considering scenes which exhibit glossy surfaces. In contrast, the rich structure of the light field with many densely sampled views allows us to cope with non-Lambertian objects by introducing an additional decomposition term that models specularity. Regularization along the epipolar plane images further encourages albedo and shading consistency across views. In evaluations of our method on real-world data sets captured with a Lytro Illum plenoptic camera, we demonstrate the advantages of our approach with respect to intrinsic image decomposition and specular removal. We present a method to separate a dichromatic reflection component from diffuse object colors for the set of rays in a 4D light field such that the separation is consistent across all subaperture views. The separation model is based on explaining the observed light field as a sparse linear combination of a constant-color specular term and a small finite set of albedos. Consistency across the light field is achieved by embedding the ray-wise separation into a global optimization framework. On each individual epipolar plane image (EPI), the diffuse coefficients need to be constant along lines which are the projections of the same scene point, while the specular coefficient needs to be constant along the direction of the specular flow within the epipolar volume. We handle both constraints with depth-dependent anisotropic regularizers, and demonstrate promising performance on a number of real-world light fields captured with a Lytro Illum plenoptic camera. Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current light-field depth estimation methods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. The standard Lambertian photoconsistency measure considers the variance of different views, effectively enforcing point-consistency , i.e., that all views map to the same point in RGB space. This variance or point-consistency condition is a poor metric for glossy surfaces. In this paper, we present a novel theory of the relationship between light-field data and reflectance from the dichromatic model. We present a physically-based and practical method to estimate the light source color and separate specularity. We present a new photo consistency metric, line-consistency , which represents how viewpoint changes affect specular points. We then show how the new metric can be used in combination with the standard Lambertian variance or point-consistency measure to give us results that are robust against scenes with glossy surfaces. With our analysis, we can also robustly estimate multiple light source colors and remove the specular component from glossy objects. We show that our method outperforms current state-of-the-art specular removal and depth estimation algorithms in multiple real world scenarios using the consumer Lytro and Lytro Illum light field cameras.