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 for intrinsic image decomposition, which aims to decompose images into reflectance and shading layers. Our input is a sequence of images with varying illumination acquired by a static camera, e.g. an indoor scene with a moving light source or an outdoor timelapse. We leverage the local color variations observed over time to infer constraints on the reflectance and solve the ill-posed image decomposition problem. In particular, we derive an adaptive local energy from the observations of each local neighborhood over time, and integrate distant pairwise constraints to enforce coherent decomposition across all surfaces with consistent shading changes. Our method is solely based on multiple observations of a Lambertian scene under varying illumination and does not require user interaction, scene geometry, or an explicit lighting model. We compare our results with several intrinsic decomposition methods on a number of synthetic and captured datasets. We present a technique for estimating intrinsic images from image+depth video, such as that acquired from a Kinect camera. Intrinsic image decomposition in this context has importance in applications like object modeling, in which surface colors need to be recovered without illumination effects. The proposed method is based on two new types of decomposition constraints derived from the multiple viewpoints and reconstructed 3D scene geometry of the video data. The first type provides shading constraints that enforce relationships among the shading components of different surface points according to their similarity in surface orientation. The second type imposes temporal constraints that favor consistency in the intrinsic color of a surface point seen in different video frames, which improves decomposition in cases of view-dependent non-Lambertian reflections. Local and non-local variants of the two constraints are employed in a manner complementary to local and non-local reflectance constraints used in previous works. Together they are formulated within a linear system that allows for efficient optimization. Experimental results demonstrate that each of the new constraints appreciably elevates the quality of intrinsic image estimation, and that they jointly yield decompositions that compare favorably to current techniques. In this paper, we explore the structure of light field (LF) and efficiently improve the performance of the pseudo sequence based lenslet image compression, by optimized sub-view rearrangement, enhanced illumination compensation and adaptive reconstruction filtering. First, the decomposed sub-view images are rearranged into a pseudo sequence according to our optimized scan order based on sub-view correlation. Second, the generated pseudo sequence is compressed with JEM codec in which we enhance the illumination compensation by adaptively selecting reference pixels used in parameter derivation. Finally, to reduce the distortions in lenslet image decomposition and reconstruction, we propose an lenslet reconstruction method by applying adaptive filters to the reconstructed lenslet images to compensate the reconstruction errors. Each filter is derived by minimizing the distortions between the original and reconstructed pixels with same geolocation in the lenslet image. Extensive experimental results show that the proposed method achieves up to 53.7 bit rate reduction over HEVC intra coding and 34.8 over JEM intra coding in terms of BDBR.