1 research outputs found
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution
Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to
generate a high-resolution (HR) HSI with higher spectral and spatial fidelity
from its low-resolution (LR) counterpart. The generative adversarial network
(GAN) has proven to be an effective deep learning framework for image
super-resolution. However, the optimisation process of existing GAN-based
models frequently suffers from the problem of mode collapse, leading to the
limited capacity of spectral-spatial invariant reconstruction. This may cause
the spectral-spatial distortion on the generated HSI, especially with a large
upscaling factor. To alleviate the problem of mode collapse, this work has
proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can
map the generated spectral-spatial features from the image space to the latent
space and produce a coupling component to regularise the generated samples.
Essentially, we treat an HSI as a high-dimensional manifold embedded in a
latent space. Thus, the optimisation of GAN models is converted to the problem
of learning the distributions of high-resolution HSI samples in the latent
space, making the distributions of the generated super-resolution HSIs closer
to those of their original high-resolution counterparts. We have conducted
experimental evaluations on the model performance of super-resolution and its
capability in alleviating mode collapse. The proposed approach has been tested
and validated based on two real HSI datasets with different sensors (i.e.
AVIRIS and UHD-185) for various upscaling factors and added noise levels, and
compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR,
BAGAN, SR- GAN, WGAN).Comment: 18 pages, 10 figure