  Intrinsic Image Decomposition is an open problem of generating the
constituents of an image. Generating reflectance and shading from a single
image is a challenging task specifically when there is no ground truth. There
is a lack of unsupervised learning approaches for decomposing an image into
reflectance and shading using a single image. We propose a neural network
architecture capable of this decomposition using physics-based parameters
derived from the image. Through experimental results, we show that (a) the
proposed methodology outperforms the existing deep learning-based IID
techniques and (b) the derived parameters improve the efficacy significantly.
We conclude with a closer analysis of the results (numerical and example
images) showing several avenues for improvement.
