Yufei Wang's repositories
ExposureDiffusion
[ICCV 2023] ExposureDiffusion: Learning to Expose for Low-light Image Enhancement
Awesome-Deep-Compression
Paper list of deep learning based image/video compression.
flare-removal
[ISCAS 2023] Removing Image Artifacts From Scratched Lens Protectors
Awesome-diffusion-model-for-image-processing
one summary of diffusion-based image processing, including restoration, enhancement, coding, quality assessment
Awesome-ICCV2023-Low-Level-Vision
A Collection of Papers and Codes in ICCV2023/2021 about low level vision
Awesome-Shadow-Removal
Collection of recent shadow removal works, including papers, codes, datasets, and metrics.
awesome-low-light-image-enhancement
This is a resouce list for low light image enhancement
Checkerboard-Context-Model-for-Efficient-Learned-Image-Compression
An unofficial pytorch implementation of CVPR2021 paper "Checkerboard Context Model for Efficient Learned Image Compression".
CompressAI
A PyTorch library and evaluation platform for end-to-end compression research
ddim
Denoising Diffusion Implicit Models
Image-Super-Resolution-via-Iterative-Refinement
Unoffical implementation about Image Super-Resolution via Iterative Refinement by Pytorch
improved-nerfmm
Unofficial & improved implementation of NeRF--: Neural Radiance Fields Without Known Camera Parameters
instant-ngp
Instant neural graphics primitives: lightning fast NeRF and more
IQA-PyTorch
PyTorch Toolbox for Image Quality Assessment, including MUSIQ, NIMA, LPIPS, DBCNN, WaDIQaM, NRQM(Ma), BRISQUE, NIQE, PI and more...
matryoshka-mm
Matryoshka Multimodal Models
MulimgViewer
MulimgViewer is a multi-image viewer that can open multiple images in one interface, which is convenient for image comparison and image stitching.
ResShift-1
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS 2023)
splat
WebGL 3D Gaussian Splat Viewer
upt
Official PyTorch implementation for paper "Efficient Two-Stage Detection of Human–Object Interactions with a Novel Unary–Pairwise Transformer"
wilds
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.