相关论文: Deep learning-based deconvolution for interferomet…

Image denoising is a classical problem in low level computer vision. Model-based optimization methods and deep learning approaches have been the two main strategies for solving the problem. Model-based optimization methods are flexible for…

Background: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image…

Convolutions are one of the most important operations in signal processing. They often involve large arrays and require significant computing time. Moreover, in practice, the signal data to be processed by convolution may be corrupted by…

The proliferation of imaging devices and countless image data generated every day impose an increasingly high demand on efficient and effective image denoising. In this paper, we establish a theoretical connection between principal…

Radiation-induced acoustic (RA) imaging is a promising technique for visualizing radiation energy deposition in tissues, enabling new imaging modalities and real-time therapy monitoring. However, it requires measuring hundreds or even…

Over the next few years the new radio telescopes, such as the Low Frequency Array (LOFAR) will greatly enhance our knowledge of the active history of the Universe. Large-area surveys with these new telescopes will no longer be dominated by…

This study investigates the sensitivity of the radio interferometers to identify imprints of spatially inhomogeneous dark matter annihilation signatures in the 21-cm signal during the pre-reionization era. We focus on the upcoming low-mode…

The shearlet transform from applied harmonic analysis is currently the state of the art when analyzing multidimensional signals with anisotropic singularities. Its optimal sparse approximation properties and its faithful digitalization…

In this work, an efficient numerical scheme is presented for seismic blind deconvolution in a multichannel scenario. The proposed method iterate with wo steps: first, wavelet estimation across all channels and second, refinement of the…

The Square Kilometer Array (SKA) would be the world's largest radio telescope with eventually over a square kilometer of collecting area. However, there are enormous challenges in its data processing. The using of modern distributed…

SKA-MID surveys will be the first in the radio domain to achieve clearly sub-arcsecond resolution at high sensitivity over large areas, opening new science applications for galaxy evolution. To investigate the potential of these surveys, we…

ThunderKAT is the image-plane transients programme for MeerKAT. The goal as outlined in 2010, and still today, is to find, identify and understand high-energy astrophysical processes via their radio emission (often in concert with…

Stellar light curves contain valuable information about oscillations and granulation, offering insights into stars' internal structures and evolutionary states. Traditional asteroseismic techniques, primarily focused on power spectral…

Understanding the structure of complex, nonstationary, high-dimensional time-evolving signals is a central challenge in scientific data analysis. In many domains, such as speech and biomedical signal processing, the ability to learn…

In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image deconvolution. We train a FCNN to remove noises in the gradient…

Multi-energy computed tomography (CT) with photon counting detectors (PCDs) enables spectral imaging as PCDs can assign the incoming photons to specific energy channels. However, PCDs with many spectral channels drastically increase the…

The ability to recover MRI signal from noise is key to achieve fast acquisition, accurate quantification, and high image quality. Past work has shown convolutional neural networks can be used with abundant and paired low and high-SNR images…

Today, image denoising by thresholding of wavelet coefficients is a commonly used tool for 2D image enhancement. Since the data product of spectroscopic imaging surveys has two spatial and one spectral dimension, the techniques for…

The advancement of imaging devices and countless image data generated everyday impose an increasingly high demand on efficient and effective image denoising. In this paper, we present a computationally simple denoising algorithm, termed…

Machine learning has become essential for automated classification of astronomical transients, but current approaches face significant limitations: classifiers trained on simulations struggle with real data, models developed for one survey…