3,209 research outputs found
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images
Because of the necessity to obtain high-quality images with minimal radiation
doses, such as in low-field magnetic resonance imaging, super-resolution
reconstruction in medical imaging has become more popular (MRI). However, due
to the complexity and high aesthetic requirements of medical imaging, image
super-resolution reconstruction remains a difficult challenge. In this paper,
we offer a deep learning-based strategy for reconstructing medical images from
low resolutions utilizing Transformer and Generative Adversarial Networks
(T-GAN). The integrated system can extract more precise texture information and
focus more on important locations through global image matching after
successfully inserting Transformer into the generative adversarial network for
picture reconstruction. Furthermore, we weighted the combination of content
loss, adversarial loss, and adversarial feature loss as the final multi-task
loss function during the training of our proposed model T-GAN. In comparison to
established measures like PSNR and SSIM, our suggested T-GAN achieves optimal
performance and recovers more texture features in super-resolution
reconstruction of MRI scanned images of the knees and belly.Comment: 8 pages and 6 figure
Transport-Based Neural Style Transfer for Smoke Simulations
Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional
materials: http://www.byungsoo.me/project/neural-flow-styl
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