3,680 research outputs found
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
In recent years, endomicroscopy has become increasingly used for diagnostic
purposes and interventional guidance. It can provide intraoperative aids for
real-time tissue characterization and can help to perform visual investigations
aimed for example to discover epithelial cancers. Due to physical constraints
on the acquisition process, endomicroscopy images, still today have a low
number of informative pixels which hampers their quality. Post-processing
techniques, such as Super-Resolution (SR), are a potential solution to increase
the quality of these images. SR techniques are often supervised, requiring
aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to
train a model. However, in our domain, the lack of HR images hinders the
collection of such pairs and makes supervised training unsuitable. For this
reason, we propose an unsupervised SR framework based on an adversarial deep
neural network with a physically-inspired cycle consistency, designed to impose
some acquisition properties on the super-resolved images. Our framework can
exploit HR images, regardless of the domain where they are coming from, to
transfer the quality of the HR images to the initial LR images. This property
can be particularly useful in all situations where pairs of LR/HR are not
available during the training. Our quantitative analysis, validated using a
database of 238 endomicroscopy video sequences from 143 patients, shows the
ability of the pipeline to produce convincing super-resolved images. A Mean
Opinion Score (MOS) study also confirms this quantitative image quality
assessment.Comment: Accepted for publication on Medical Image Analysis journa
NAM: Non-Adversarial Unsupervised Domain Mapping
Several methods were recently proposed for the task of translating images
between domains without prior knowledge in the form of correspondences. The
existing methods apply adversarial learning to ensure that the distribution of
the mapped source domain is indistinguishable from the target domain, which
suffers from known stability issues. In addition, most methods rely heavily on
`cycle' relationships between the domains, which enforce a one-to-one mapping.
In this work, we introduce an alternative method: Non-Adversarial Mapping
(NAM), which separates the task of target domain generative modeling from the
cross-domain mapping task. NAM relies on a pre-trained generative model of the
target domain, and aligns each source image with an image synthesized from the
target domain, while jointly optimizing the domain mapping function. It has
several key advantages: higher quality and resolution image translations,
simpler and more stable training and reusable target models. Extensive
experiments are presented validating the advantages of our method.Comment: ECCV 201
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