jinyeying's repositories
night-enhancement
[ECCV2022] "Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression", https://arxiv.org/abs/2207.10564
DC-ShadowNet-Hard-and-Soft-Shadow-Removal
[ICCV2021]"DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network", https://arxiv.org/abs/2207.10434
FogRemoval
[ACCV22] Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal, https://arxiv.org/abs/2210.03061
nighttime_dehaze
[ACMMM2023] "Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution", https://arxiv.org/abs/2308.01738
Awesome-Nighttime-Enhancement
Collection of recent nighttime enhancement works, including papers, codes, datasets, and metrics.
S-Aware-network
[AAAI23] Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning, https://arxiv.org/abs/2211.14751
DeS3_Deshadow
[AAAI'2024] DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity. First diffusion-based shadow removal performs robustly on hard, soft and self shadows. https://arxiv.org/abs/2211.08089
jinyeying.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Awesome-Diffusion-Models
A collection of resources and papers on Diffusion Models
Awesome-Shadow-Removal
Collection of recent shadow removal works, including papers, codes, datasets, and metrics.
Inpaint-Anything
Inpaint anything using Segment Anything and inpainting models.
Awesome-diffusion-model-for-image-processing
one summary of diffusion-based image processing, including restoration, enhancement, coding, quality assessment
Improving-Lens-Flare-Removal
Official implementation of ICCV 2023 Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery
SeD
Semantic-Aware Discriminator for Image Super-Resolution
VTprompt
The code for paper:Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models