Abstract
We introduce a new network structure for decomposing
an image into its intrinsic albedo and shading. We treat it as
an image-to-image transformation problem and explore the
scale space of the input and output. By expanding the output images (albedo and shading) into their Laplacian pyramid components, we develop a multi-channel architecture
that learns the image-to-image transformation function in
successive frequency bands in parallel, within each channel
is a fully convolutional neural network. This network architecture is general and extensible, and has demonstrated
excellent performance on the task of intrinsic image decomposition. We evaluate the network on two benchmark
datasets: the MPI-Sintel dataset and the MIT Intrinsic Images dataset. Both quantitative and qualitative results show
our model delivers a clear progression over state-of-the-art