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Found 7 papers, 3 papers with code
Information-Theoretic GAN Compression with Variational Energy-based Model
We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model.
Designing Phase Masks for Under-Display Cameras
Diffractive blur and low light levels are two fundamental challenges in producing high-quality photographs in under-display cameras (UDCs).
Controllable Image Restoration for Under-Display Camera in Smartphones
However, this causes inevitable image degradation in the form of spatially variant blur and noise because of the opaque display in front of the camera.
Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography
Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded.
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge.
Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally complex because of the repeated use of the forward and backward projection.
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction
To the best of our knowledge, this work is the first deep learning architecture for low-dose CT reconstruction that has been rigorously evaluated and proven for its efficacy.
