CNN based Learning using Reflection and Retinex Models for Intrinsic Image
Decomposition
Abstract
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-and-out black box and do not consider the wellestablished, traditional image formation process as the basis of their intrinsic learning process. As a consequence,
although current deep learning approaches show superior
performance when considering quantitative benchmark results, traditional approaches are still dominant in achieving
high qualitative results.
In this paper, the aim is to exploit the best of the two
worlds. A method is proposed that (1) is empowered by
deep learning capabilities, (2) considers a physics-based
reflection model to steer the learning process, and (3) exploits the traditional approach to obtain intrinsic images
by exploiting reflectance and shading gradient information.
The proposed model is fast to compute and allows for the
integration of all intrinsic components. To train the new
model, an object centered large-scale datasets with intrinsic ground-truth images are created.
The evaluation results demonstrate that the new model
outperforms existing methods. Visual inspection shows that
the image formation loss function augments color reproduction and the use of gradient information produces sharper
edges.
Datasets, models and higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.