Source code for book "Image algorithms for low-level vision tasks" (Jia. 2024)
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Updated
May 7, 2024 - Python
Source code for book "Image algorithms for low-level vision tasks" (Jia. 2024)
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.
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.
Enhancing satellite image clarity by removing haze using AOD-Net's deep convolutional and residual architectures
Enhancing satellite image clarity by removing haze using AOD-Net's deep convolutional and residual architectures
InstructIR: High-Quality Image Restoration Following Human Instructions https://huggingface.co/spaces/marcosv/InstructIR
[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 "SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing"
Enhance Images with Javascript and AI. Increase resolution, retouch, denoise, and more. Open Source, Browser & Node Compatible, MIT License.
This is a novel methodology to perform dehazing process on a single outdoor Image using feature extraction techniques in Deep Learning and 3 added pre-processing steps.
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".
Dehazing enhances surveillance and remote sensing by improving image clarity for better detection and analysis.
Video Enhancement For Surveillance
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.
This is an improved version of the deblurring of faces. It shows about 5% increase in SSIM metric in comparison with the original methods. Tweaked the existing dehazing algorithms to work for deblurring.
The Code is created for dehaze, sand dust image and underwater image enhancement.
An implementation of Fattal's 'Dehazing using Color-Lines'
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