Heykx8

Heykx8's Stars

• jianxu305/nCov2019_analysis

  Analysis of 2019-nCov coronavirus data

  Language:HTML11766

• huipengzhang/COCO2XML

  Converting JSON of COCO dataset into XML of PASCAL VOC

  Language:Python114

• murphypei/caltech-pascal-voc-converter

  convert caltech dataset(videos) to voc2007 dataset(images) format

  Language:Python3

• chenyuntc/simple-faster-rcnn-pytorch

  A simplified implemention of Faster R-CNN that replicate performance from origin paper

  Language:Jupyter Notebook4k1.1k

• liubin0314/Knowledge-Modeling-and-Knowledge-Management

  《知识管理与知识建模》课程资源。同学们也可以在这里讨论技术问题。

  138

• smilelight/lightKG

  基于Pytorch和torchtext的知识图谱深度学习框架。

  Language:Python612151

• bojone/kg-2019

  2019年百度的三元组抽取比赛，“科学空间队”源码

  Language:Python765184

• lemonhu/stock-knowledge-graph

  利用网络上公开的数据构建一个小型的证券知识图谱/知识库

  Language:Python2k597

• nightrome/matconvnet-calvin

  Code for several state-of-the-art papers in object detection and semantic segmentation.

  Language:Matlab8672

• koujan/Robotics-Course-project

  Haze can cause poor visibility and loss of contrast in images and videos. In this article, we study the dehazing problem which can improve visibility and thus help in many computer vision applications. An extensive comparison of state of the art single image dehazing methods is done. One simple contrast enhancement method is used for dehazing. Structure- texture decomposition has been used in conjunction with this enhancement method to improve its performance in presence of synthetic noise. Methods which use a haze formation model and attempt at solving an ill-posed problem using computer vision priors are also investigated. The two priors studied are dark channel prior and the non-local prior. Both qualitative and quantitative comparisons for atmospheric and underwater images on all three methods provide a conclusive idea of which dehazing method performs better. All this knowledge has been extended to video dehazing. A video dehazing method which uses the spatial and temporal information in a video is studied in depth. An improved version of video dehazing is proposed in this article, which uses the spatial-temporal information fusion framework but does not suffer from some of its limitations. The new video dehazing method is shown to produce better results on test videos

  Language:Matlab3413

• Chrisawa/image-video-dehazing-OpenCV

  image and video dehazing by median dark channel prior proposed by Gibson et al.

  3120

• zlinker/DehazeNet

  an end to end dehaze tools

  Language:Python12851

• allenyangyl/dehaze

  2328

• caibolun/DehazeNet

  DehazeNet: An End-to-End System for Single Image Haze Removal

  Language:C++25256

• TanayNarshana/Single-Image-Dehazing-1

  Single Image Dehazing using Dark Channel Prior based on the paper Single Image Haze Removal using Dark Channel Prior, by Kaiming He, Jian Sun, and Xiaoou Tang, in CVPR 2009

  Language:Matlab4

• rrwiyatn/Image-Dehazing-Dark-Channel-Prior

  Dehazing algorithm based on a research paper titled "Single Image Haze Removal Using Dark Channel Prior". Published by: Kaiming He, Jian Sun, Xiaoou Tang, The Chinese University of Hong Kong, and Microsoft Research Asia

  Language:C++122

• Htiango/cvpr09-defog

  Defog model based on the paper from He Kaiming

  Language:Matlab31

• Rock-100/Single-Image-Haze-Removal-Using-Dark-Channel-Prior

  [CVPR 2009] Single Image Haze Removal Using Dark Channel Prior

  Language:C++5614

• YuwveHu/Retinex_Mat

  Retinex图像增强Mat版

  Language:C++2

• upcAutoLang/MSRCR-Restoration

  Retinex Algorithms: C++ source code of SSR, MSR, MSRCR algorithm

  Language:C++17892

• coderSkyChen/darkchannel_dehaze

  根据论文：何等人的《Single Image Haze Removal Using Dark Channel Prior》 使用C++实现暗通道去雾算法

  85