Deep GLR combines graph Laplacian regularization with three lightweight CNN modules in a proximal optimization framework to reach 30.70 dB PSNR on LoDoPaB-CT using 5.8x fewer parameters and 30x less data per dB gain than typical deep methods.
Low-dose CT image reconstruc- tion using vector quantized convolutional autoencoder with perceptual loss,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization
Deep GLR combines graph Laplacian regularization with three lightweight CNN modules in a proximal optimization framework to reach 30.70 dB PSNR on LoDoPaB-CT using 5.8x fewer parameters and 30x less data per dB gain than typical deep methods.