  Intrinsic image decomposition is the process of recovering the image
formation components (reflectance and shading) from an image. Previous methods
employ either explicit priors to constrain the problem or implicit constraints
as formulated by their losses (deep learning). These methods can be negatively
influenced by strong illumination conditions causing shading-reflectance
leakages.
  Therefore, in this paper, an end-to-end edge-driven hybrid CNN approach is
proposed for intrinsic image decomposition. Edges correspond to illumination
invariant gradients. To handle hard negative illumination transitions, a
hierarchical approach is taken including global and local refinement layers. We
make use of attention layers to further strengthen the learning process.
  An extensive ablation study and large scale experiments are conducted showing
that it is beneficial for edge-driven hybrid IID networks to make use of
illumination invariant descriptors and that separating global and local cues
helps in improving the performance of the network. Finally, it is shown that
the proposed method obtains state of the art performance and is able to
generalise well to real world images. The project page with pretrained models,
finetuned models and network code can be found at
https://ivi.fnwi.uva.nl/cv/pienet/.
