Shape Measurement of Radio Galaxies Using Equivariant CNNs
32ND EUROPEAN SIGNAL PROCESSING CONFERENCE, EUSIPCO 2024（2024）
Key words
Convolutional Neural Network,Shape Measures,Radio Galaxies,Neural Network,Biased Estimates,Measurement Bias,Simulation Accuracy,Real Observations,Gravitational Lensing,Image Features,Deconvolution,Convolutional Layers,Fast Fourier Transform,Image Reconstruction,Dense Layer,Line-of-sight,Reconstruction Process,Point Spread Function,Convolutional Neural Network Layers,Kernel Weight,Feature Extraction Layer,Traditional Convolutional Neural Network,Radio Astronomy,Latent Vector,Random Weights,Layer Of Autoencoder,Traditional Convolution
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