A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning improves 3D microscopy resolution without matched target images.
Study shows zero-shot super-resolution in neural operators is impossible in many cases.
Neural DEs improve single image super-resolution.
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
A new method uses conformal prediction to create reliable confidence masks for image super-resolution.
New framework compresses and recovers scientific data efficiently.
Generative adversarial networks generate realistic, time-evolving high-resolution atmospheric fields.
Deep learning speeds up whole heart MRI to 30 seconds.
Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal conv…
MeshfreeFlowNet generates high-resolution spatio-temporal solutions from low-resolution inputs.
Paper proposes SMFN for high-res spherical video super-resolution.
A cost-effective method to generate high-resolution images using wavelet-based super-resolution.
GANs can learn hierarchical distributions in real-world images efficiently.
SRFRN accelerates image super-resolution using shallow residual units.
This paper improves image super-resolution by integrating cross-scale non-local attention.
Enhances VAEs for sharper image synthesis.
Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a nonhomogeneous Poisson process. The estimator is the super-resolution analogue to Shao 2010 and Shao & Lii 2011, which is a sum of sinusoids where and the frequency, amplitud…
Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns, called dictionary elements, from the data. Because it is nonparametric…
New neural network captures spatial correlations in wind speed predictions.
Deep learning solves wave-based inverse problems, including super-resolution imaging.
With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often di…
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images. Current state-of-the-art methods process a batch of LR frames to generate a single high-resolu…
Probabilistic SR method speeds up high-fidelity simulations with reliable uncertainty estimates.
Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an altern…
DeepAM optimizes deep neural networks for image super-resolution.
HighRes-net enhances satellite imagery by fusing multiple low-res views.
As Super-Resolution (SR) has matured as a research topic, it has been applied to additional topics beyond image reconstruction. In particular, combining classification or object detection tasks with a super-resolution preprocessing stage has yielded improvements in accuracy especially with objects that are small relati…
With the fast growth in the visual surveillance and security sectors, thermal infrared images have become increasingly necessary ina large variety of industrial applications. This is true even though IR sensors are still more expensive than their RGB counterpart having the same resolution. In this paper, we propose a d…
Time-aware deep learning methods improve spatial downscaling of atmospheric pollutants.
The paper examines Randers metrics with isotropic scalar curvature properties.
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework th…
Over the past decade, many Super Resolution techniques have been developed using deep learning. Among those, generative adversarial networks (GAN) and very deep convolutional networks (VDSR) have shown promising results in terms of HR image quality and computational speed. In this paper, we propose two approaches based…
Paper establishes a relation between Berwald scalar curvature and S-curvature.
In this paper, we find a condition on -metrics under which the notions of isotropic S-curvature, weakly isotropic S-curvature and isotropic mean Berwald curvature are equivalent.
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions. Current Video Super-Resolution methods are not robust to mismatch between training and testing degradation models since they are trained against a single…
Study physical work done by isotropic vector forces along isotropic curves.
High resolution magnetic resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware, cost and processing constraints. Recently, deep learning methods have been shown to produce compelling state of the art results for image super-resolution. Paying pa…
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-res…
Study isotropic Riemannian maps and helices along them.
With the recent advancement in the deep learning technologies such as CNNs and GANs, there is significant improvement in the quality of the images reconstructed by deep learning based super-resolution (SR) techniques. In this work, we propose a robust loss function based on the preservation of edges obtained by the Can…
We study two types of isotropic planes: weakly isotropic and strongly isotropic planes. We prove that a Riemannian manifold of indefinite metric is conformally flat if and only if its curvature tensor vanishes on all the strongly isotropic planes. We specialize the plane axiom for Riemannian manifolds of indefinite met…
Novel algorithm accelerates PnP methods for image deblurring and super-resolution.
Study isotropic curves on complex quadric with geometric relations.
In this paper, we construct a new class of Finsler manifolds called generalized isotropic Berwald manifolds which is an extension of the class of isotropic Berwald manifolds. We prove that every generalized isotropic Berwald manifold is a generalized Douglas-Weyl manifold. On a compact generalized isotropic Berwald man…
Constructs a moment map flow for isotropic maps on surfaces.
Spinor representation in isotropic space via Laguerre geometry.
The paper studies hanging chains and surfaces in degenerate geometries.
Super-resolution fluorescence microscopy, with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods stil…