Compgen: Synthesis and Generation of Faces From Edgemaps

Abhiram Rao Gorle

Abstract:

We address the problem of synthesis and generation of faces
from edgemaps, motivated by extreme low bit-rate facial compres-
sion and the need for robust source-channel coding over noisy chan-
nels. Three approaches for image reconstruction are proposed. In the
first, a deep learning-based encoder-decoder creates a latent space
representation of the original image. An Edgemap-to-Latent Mapper
(ELM) network maps the input edgemap to this latent space, with the
final image reconstructed using a pre-trained compressive decoder.
The second approach retrains the compressive decoder to reconstruct
images from the ELM network’s output. The third approach jointly
trains the ELM network and decoder, enabling direct reconstruction
from the edgemap. This end-to-end framework achieves reason-
able reconstruction fidelity. We also examine the impact of additive
channel noise on edgemap transmission under low SNR conditions,
demonstrating that even with significant noise, a DNN-based joint
denoiser and edgemap decoder can reconstruct images. At extremely
low SNRs, where edgemaps are highly corrupted, the network also
exhibits generative capabilities, producing plausible images.