16,018 research outputs found
MedGAN: Medical Image Translation using GANs
Image-to-image translation is considered a new frontier in the field of
medical image analysis, with numerous potential applications. However, a large
portion of recent approaches offers individualized solutions based on
specialized task-specific architectures or require refinement through
non-end-to-end training. In this paper, we propose a new framework, named
MedGAN, for medical image-to-image translation which operates on the image
level in an end-to-end manner. MedGAN builds upon recent advances in the field
of generative adversarial networks (GANs) by merging the adversarial framework
with a new combination of non-adversarial losses. We utilize a discriminator
network as a trainable feature extractor which penalizes the discrepancy
between the translated medical images and the desired modalities. Moreover,
style-transfer losses are utilized to match the textures and fine-structures of
the desired target images to the translated images. Additionally, we present a
new generator architecture, titled CasNet, which enhances the sharpness of the
translated medical outputs through progressive refinement via encoder-decoder
pairs. Without any application-specific modifications, we apply MedGAN on three
different tasks: PET-CT translation, correction of MR motion artefacts and PET
image denoising. Perceptual analysis by radiologists and quantitative
evaluations illustrate that the MedGAN outperforms other existing translation
approaches.Comment: 16 pages, 8 figure
Demodulation of Spatial Carrier Images: Performance Analysis of Several Algorithms Using a Single Image
http://link.springer.com/article/10.1007%2Fs11340-013-9741-6#Optical full-field techniques have a great importance in modern experimental mechanics. Even if they are reasonably spread among the university laboratories, their diffusion in industrial companies remains very narrow for several reasons, especially a lack of metrological performance assessment. A full-field measurement can be characterized by its resolution, bias, measuring range, and by a specific quantity, the spatial resolution. The present paper proposes an original procedure to estimate in one single step the resolution, bias and spatial resolution for a given operator (decoding algorithms such as image correlation, low-pass filters, derivation tools ...). This procedure is based on the construction of a particular multi-frequential field, and a Bode diagram representation of the results. This analysis is applied to various phase demodulating algorithms suited to estimate in-plane displacements.GDR CNRS 2519 “Mesures de Champs et Identification en Mécanique des Solide
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
We propose semantic region-adaptive normalization (SEAN), a simple but
effective building block for Generative Adversarial Networks conditioned on
segmentation masks that describe the semantic regions in the desired output
image. Using SEAN normalization, we can build a network architecture that can
control the style of each semantic region individually, e.g., we can specify
one style reference image per region. SEAN is better suited to encode,
transfer, and synthesize style than the best previous method in terms of
reconstruction quality, variability, and visual quality. We evaluate SEAN on
multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than
the current state of the art. SEAN also pushes the frontier of interactive
image editing. We can interactively edit images by changing segmentation masks
or the style for any given region. We can also interpolate styles from two
reference images per region.Comment: Accepted as a CVPR 2020 oral paper. The interactive demo is available
at https://youtu.be/0Vbj9xFgoU
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