3,933 research outputs found
TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition
This work tackles the face recognition task on images captured using thermal
camera sensors which can operate in the non-light environment. While it can
greatly increase the scope and benefits of the current security surveillance
systems, performing such a task using thermal images is a challenging problem
compared to face recognition task in the Visible Light Domain (VLD). This is
partly due to the much smaller amount number of thermal imagery data collected
compared to the VLD data. Unfortunately, direct application of the existing
very strong face recognition models trained using VLD data into the thermal
imagery data will not produce a satisfactory performance. This is due to the
existence of the domain gap between the thermal and VLD images. To this end, we
propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is
able to transform thermal face images into their corresponding VLD images
whilst maintaining identity information which is sufficient enough for the
existing VLD face recognition models to perform recognition. Some examples are
presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an
explicit closed-set face recognition loss to regularize the discriminator
network training. This information will then be conveyed into the generator
network in the forms of gradient loss. In the experiment, we show that by using
this additional explicit regularization for the discriminator network, the
TV-GAN is able to preserve more identity information when translating a thermal
image of a person which is not seen before by the TV-GAN
GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis
Histopathological cancer diagnosis is based on visual examination of stained
tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely
employed worldwide. It is easy to acquire and cost effective, but cells and
tissue components show low-contrast with varying tones of dark blue and pink,
which makes difficult visual assessments, digital image analysis, and
quantifications. These limitations can be overcome by IHC staining of target
proteins of the tissue slide. IHC provides a selective, high-contrast imaging
of cells and tissue components, but their use is largely limited by a
significantly more complex laboratory processing and high cost. We proposed a
conditional CycleGAN (cCGAN) network to transform the H\&E stained images into
IHC stained images, facilitating virtual IHC staining on the same slide. This
data-driven method requires only a limited amount of labelled data but will
generate pixel level segmentation results. The proposed cCGAN model improves
the original network \cite{zhu_unpaired_2017} by adding category conditions and
introducing two structural loss functions, which realize a multi-subdomain
translation and improve the translation accuracy as well. % need to give
reasons here. Experiments demonstrate that the proposed model outperforms the
original method in unpaired image translation with multi-subdomains. We also
explore the potential of unpaired images to image translation method applied on
other histology images related tasks with different staining techniques
- …