Separating an RGB image into its albedo and shading maps abstracts the idea behind
intrinsic image decomposition. It is a prolonged and obscure computer vision problem for …

Separating an RGB image into its albedo and shading maps abstracts the idea behind
intrinsic image decomposition. It is a prolonged and underdetermined problem in computer …

Intrinsic image decomposition is a highly ill-posed problem in computer vision referring to
extract albedo and shading from an image. In this paper, we regard it as an image-to-image …

Intrinsic image decomposition is the process of separating the reflectance and shading
layers of an image, which is a challenging and underdetermined problem. In this paper, we …

Intrinsic image decomposition is the decomposition of an image into its reflectance and
shading components. The intrinsic image decomposition problem is inherently ill-posed …

We introduce a new network structure for decomposing an image into its intrinsic albedo and
shading. We treat this as an image-to-image transformation problem and explore the scale …

Intrinsic image decomposition refers to recover the albedo and shading from images, which
is an ill-posed problem in signal processing. As realistic labeled data are severely lacking, it …

The goal of intrinsic image decomposition is to recover low level features of images. Most of
the studies tend to consider only reflectance and shading, even though it is known that …

This paper proposes a deep convolutional network for intrinsic image decomposition from a
single image. To maintain the visual details of a result, we approach the problem from the …

Most of the traditional work on intrinsic image decomposition rely on deriving priors about
scene characteristics. On the other hand, recent research use deep learning models as in …