The key to the depth estimation from a single image lies in inferring the distance
of various objects without copying texture while maintaining clear object boundaries.
In this paper, we propose depth estimation from a single image using edge extraction
network and dark channel prior (DCP). We build an edge extraction network based on
generative adversarial networks (GANs) to select valid depth edges from a number of
edges in an image. We use DCP to generate a transmission map that is able to represent
distance from the camera. Transmission map is generated by conducting minimum value
filtering on DCP. First, we concatenate the transmission map with the original RGB
image to form a tensor, i.e. RGB + T. Second, we generate an initial depth image from
the tensor through the generator by inferring depth from stacked residual blocks.
Third, we compare the edge map of the initial depth image with that of the input RGB
image to select valid depth edges. Both edge maps are generated by the edge extraction
network. Finally, we distinguish real and fake on the generated depth image using
a discriminator, and enhances the performance of the generator. Various experiments
on NYU, Make3D and MPI Sintel datasets demonstrate that the proposed network generates
clear edges in depth images as well as outperforms state-of-the-art methods in terms
of visual quality and quantitative measurements.