title: Colorful Diffuse Intrinsic Image Decomposition in the Wild
summary: 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.
paper_authors._id: 66f0e6b12e2c3d8539f6f815
paper_authors.name: Chris Careaga
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-23T07:44:16.008Z
paper_authors._id: 655e75ea56e5ceaf052536f0
paper_authors.avatarUrl: /avatars/ab3f84eef186f839f1dff5cf348c38c4.svg
paper_authors.fullname: Chris Careaga
paper_authors.type: user
paper_authors.user: ccareaga
paper_authors._id: 66f0e6b12e2c3d8539f6f816
paper_authors.name: Yağız Aksoy
paper_title: Colorful Diffuse Intrinsic Image Decomposition in the Wild
paper_summary: 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.
paper_ai_summary: A model decomposes images into diffuse albedo, colorful shading, and specular components, facilitating illumination-aware image editing despite limited data.
paper_ai_keywords: intrinsic image decomposition diffuse albedo diffuse shading specular residual single-color illumination Lambertian world