Haze Removal can remove slight to extreme cases of haze affecting an image. Its most typical use is for landscape photography where the haze causes low contrast and low saturation, but it can also be used to improve images taken during rainy and foggy conditions.
This code cotains the implementation of single and multiple dehazing processing of image(s). Note: Remember to modify the path to your directory folder.
original images
dehazed images
This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Secti
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The code in this toolbox implements the "Spectralformer: Rethinking hyperspectral image classification with transformers". More specifically, it is detailed as follow.
Nasir Khusraw : Travelling Salesman Problem The TSP solved using genetic algorithm. This project show TSP path overlaid on a map of the Iran provinces
TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision
The trained model and denoising example for paper : Cardiopulmonary Auscultation Enhancement with a Two-Stage Noise Cancellation Approach
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CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi
TAP: Text-Aware Pre-training TAP: Text-Aware Pre-training for Text-VQA and Text-Caption by Zhengyuan Yang, Yijuan Lu, Jianfeng Wang, Xi Yin, Dinei Flo
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PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers Created by Xumin Yu*, Yongming Rao*, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie Zhou
3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks Introduction This repository contains the code and models for the follo
Differentiable Wavetable Synthesis
FSL-Mate is a collection of resources for few-shot learning (FSL). In particular, FSL-Mate currently contains FewShotPapers: a paper list which tracks
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