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 Study abstract:Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies.The produced blurry images may obscure the shapes of astronomical objects and cause measurement errors.

The AI tool also enables faster and more accurate image processing by utilizing computational power to sift through image data.This ill-posed linear inverse problem is usually solved with deconvolution algorithms enhanced by regularisation priors or deep learning.We characterize the time-performance trade-off of several methods for galaxies of differing brightness levels.

scientists from Northwestern University and Tsinghua University may have found a solution to this vexing problem.We introduce a so-called physics-informed deep learning approach to the Point Spread Function (PSF) deconvolution problem in galaxy surveys.

in which a neural network learns appropriate hyperparameters and denoising priors from simulated galaxy images.

We apply algorithm unrolling and the Plug-and-Play technique to the Alternating Direction Method of Multipliers (ADMM).Xu found this was due to the stretching and folding process irreversibly disrupting the interface between some silica particles and the polymer matrix.

We report a strategy that allows making three dimensional transparent glass with origami techniques.Our process starts from a dynamic covalent polymer matrix with homogeneously dispersed silica nanoparticles.

chemical engineers have expanded the age-old method to create complicated structures from glass and other rigid materials.the ceramics and artistic communities would use it to design sculptures and catalysts as well as other things the researchers haven’t even considered