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.
Intrinsic decomposition of the shading and albedo components of an image is a long-standing problem in computer vision and graphics since it was formulated by Barrow and Tenembaum in the 70s @cite_33 . We review previous intrinsic decomposition algorithms based on their input, and then briefly cover related light field processing.
@cite_33
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We suggest that an appropriate role of early visual processing is to describe a scene in terms of intrinsic (vertical) characteristics -- such as range, orientation, reflectance, and incident illumination -- of the surface element visible at each point in the image. Support for this idea comes from three sources: the obvious utility of intrinsic characteristics for higher-level scene analysis; the apparent ability of humans to determine these characteristics, regardless of viewing conditions or familiarity with the scene; and a theoretical argument that such a description is obtainable, by a noncognitive and nonpurposive process, at least, for simple scene domains. The central problem in recovering intrinsic scene characteristics is that the information is confounded in the original light-intensity image: a single intensity value encodes all the characteristics of the corresponding scene point. Recovery depends on exploiting constraints, derived from assumptions about the nature of the scene and the physics of the imaging process.