A no-reference version of HDR-VDP using deep-learning
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Updated
Jul 31, 2024 - Python
A no-reference version of HDR-VDP using deep-learning
This is a very simplified ipynb code for KupynOrest's Deblur GAN code. DeblurGAN addresses the challenge of end-to-end image deblurring through the use of conditional Generative Adversarial Networks (cGANs).I have used pytorch for this implementation.
This project features an automatic image colorization model utilizing a U-Net architecture combined with perceptual loss for enhanced colorization quality.
LPIPS metric. pip install lpips
Single Image Reflection Separation with Perceptual Losses
PyTorch implementation of the Perceptual Evaluation of Speech Quality for wideband audio
Generate data, PSNR, Perceptual Loss, Unet
Implementation of VAE and Style-GAN Architecture Achieving State of the Art Reconstruction
ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks, published in ECCV 2018) implemented in Tensorflow 2.0+. This is an unofficial implementation. With Colab.
Official implementation of RDST. A residual dense swin transformer for medical image super-resolution
[ACM MM 20 Oral] PyTorch implementation of Self-supervised Dance Video Synthesis Conditioned on Music
Android librarry (kotlin) : Image (JPEG, BMP) comparison (perceptual hash algorithm)
single image super resolution
StyleGAN Encoder - converts real images to latent space
Pytorch implementation of Neural Style Transfer (NST). Reviewing litterature and implementing some ideas.
StarNet
🎨 Implementation of Fast Neural Style Transfer proposed by Justin Johnson et al. in the paper Perceptual Losses for Real-Time Style Transfer and Super-Resolution
A perceptual weighting filter loss for DNN training in speech enhancement
Implementation of the fast neural style transfer algorithm on Keras. Includes Jupyter notebooks, python script and web app.
Demake-up Filter Use Unet model, Resnet50 Pretrained
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