Code Submission for UCLA CS245: Big Data Analytics course offered in Fall 2023
This is an unofficial implementation of Image Super-Resolution via Iterative Refinement(SR3) by PyTorch.
The other branches contain updated code for each specific experiments (zero_snr, splitImage, lazcos, bilinear_unet, and area-bicubic-upsampling-unet). This master branch only contains the unedited version of the unofficial code for the SR3 Paper (https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement)
Please refer to each branch for advancements to SR3
## Acknowledgements
Our work is based on the following theoretical works:
- [Denoising Diffusion Probabilistic Models](https://arxiv.org/pdf/2006.11239.pdf)
- [Image Super-Resolution via Iterative Refinement](https://arxiv.org/pdf/2104.07636.pdf)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://arxiv.org/abs/2009.00713)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://arxiv.org/abs/1809.11096)
Furthermore, we are benefitting a lot from the following projects:
- https://github.com/bhushan23/BIG-GAN
- https://github.com/lmnt-com/wavegrad
- https://github.com/rosinality/denoising-diffusion-pytorch
- https://github.com/lucidrains/denoising-diffusion-pytorch
- https://github.com/hejingwenhejingwen/AdaFM