[ECCV 2024] OneRestore: A Universal Restoration Framework for Composite Degradation
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Updated
Jul 27, 2024 - Python
[ECCV 2024] OneRestore: A Universal Restoration Framework for Composite Degradation
[ECCV 2024] Histoformer: Restoring Images in Adverse Weather Conditions via Histogram Transformer
AI for GNU Image Manipulation Program
[AAAIW 2022] DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation
[CVPRW 2023] SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing
[TII 2022] Deep Network-Enabled Haze Visibility Enhancement for Visual IoT-Driven Intelligent Transportation Systems
This is the source code of PMS-Net: Robust Haze Removal Based on Patch Map for Single Images which has been published in CVPR 2019 Long Beach
This is the official PyTorch implementation of DehazeDCT. Our method achieves the second best performance in NTIRE 2024 Dense and NonHomogeneous Dehazing Challenge (CVPR workshop))
Source code for book "Image algorithms for low-level vision tasks" (Jia. 2024), including denoising, super-resolution, dehazing, image composition and enhancement models and algorithms implemented in pure Python.
A python project to desmoke/dehaze image from the selected directory with human being and animal detection for the rescue operation during fire outbreaks or disasters etc. It can also be used for the normal dehazing operation on images.
PyTorch implementation for All-In-One Image Restoration for Unknown Corruption (AirNet) (CVPR 2022)
This is the project page of our paper which has been published in ECCV 2020.
This paper is accepted by ICCV 2021.
Image Restoration with Mean-Reverting Stochastic Differential Equations, ICML 2023. Winning solution of the NTIRE 2023 Image Shadow Removal Challenge.
[ACCV22] Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal, https://arxiv.org/abs/2210.03061
This is the python code corresponding to the article "Deep learning-driven surveillance quality enhancement for maritime management promotion under low-visibility weathers ".
This is the python code corresponding to the article "Let You See in Haze and Sandstorm: Two-in-One Low-visibility Enhancement Network".
In this Project, important algorithms such as Canny Edge Detection, Harris Corner Detection, Segmentation, and Dehazing are utilized. These algorithms perform operations like detecting edges and corners in images, segmenting different regions, and enhancing foggy or blurred images.
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