Abstract
In this paper, we focus on the intrinsic image decomposition problem for stereoscopic image pairs. The existing methods cannot be applied directly to decompose stereoscopic images, as it often produces inconsistent reflectance (albedo) and 3D artifacts after the decomposition. We propose a straightforward yet effective framework that enables a high-quality decomposition for stereoscopic pairs. First, retinex-based constraints are employed to coarsely classify the observed image gradients into two categories that are caused by reflectance changes and illumination variations, respectively. Second, reflectance-consistent constraints are added to control the reflectance consistency between the left and right views. Since this problem is highly ill-posed, we further analyze local and non-local image textures regularized by super-pixels within and across two views to reduce reflectance ambiguity. Lastly, absolute-scale constraints are employed to normalize the decomposition results. Extensive experiments on the real-world stereoscopic images and synthetic stereoscopic images reveal that our method can readily achieve high-quality decomposition performance.
Highlights
From the perspective of image formation, several key factors, such as surface material and textures, lighting conditions, shape of objects, and viewing angle, jointly determine the final appearance of the scene
Surface material and textures are usually characterized by reflectance image, while the composite product of lighting conditions and shape of objects are described by shading image
Since the real-world stereoscopic images are hard to obtain the ground-truth of their reflectance and shading, Fig. 4 only shows the visual results
Summary
From the perspective of image formation, several key factors, such as surface material and textures, lighting conditions, shape of objects, and viewing angle, jointly determine the final appearance of the scene. Due to unpleasant lighting conditions, many photographs have serious shadow regions, which greatly degrade the performance of the classic retinex algorithm To overcome this problem, Some works presented a new intrinsic image decomposition algorithm by adopting image sequences [6] or videos [2], [49]. Since the intrinsic decomposition is a highly ill-posed problem, it is hard to separate a stereoscopic image pair il,r into the reflectance rl,r and the shading sl,r only on the basis of Equ. 1 To solve this problem, we propose an algorithm that decomposes a stereoscopic image pair into the reflectance component and the shading component by optimizing the following energy with respect to the shading component sl,r : arg min sl,r.
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