Colorful Diffuse Intrinsic Image Decomposition in the Wild
This work addresses the challenge of illumination-aware image editing for applications like specularity removal and per-pixel white balancing, though it is incremental by extending prior models to handle colorful illumination and non-Lambertian assumptions.
The paper tackles the problem of intrinsic image decomposition in real-world conditions by separating an image into diffuse albedo, colorful diffuse shading, and specular residual components, achieving in-the-wild colorful diffuse shading estimation despite limited ground-truth datasets.
Intrinsic image decomposition aims to separate the surface reflectance and the effects from the illumination given a single photograph. Due to the complexity of the problem, most prior works assume a single-color illumination and a Lambertian world, which limits their use in illumination-aware image editing applications. In this work, we separate an input image into its diffuse albedo, colorful diffuse shading, and specular residual components. We arrive at our result by gradually removing first the single-color illumination and then the Lambertian-world assumptions. We show that by dividing the problem into easier sub-problems, in-the-wild colorful diffuse shading estimation can be achieved despite the limited ground-truth datasets. Our extended intrinsic model enables illumination-aware analysis of photographs and can be used for image editing applications such as specularity removal and per-pixel white balancing.