聚惠星商城 DTS-SHOP，基于 微信小程序 + springboot + vue 技术构建 ，支持单店铺，多店铺入驻的商城平台。项目包含 微信小程序，管理后台。基于java后台语言，已功能闭环，且达到商用标准的一套项目体系。
FastBee开源物联网平台，简单易用，更适合中小企业和个人学习使用。适用于智能家居、智慧办公、智慧社区、农业监测、水利监测、工业控制等。
🎉 基于Spring Boot、Spring Cloud & Alibaba的分布式微服务架构权限管理系统，同时提供了 Vue3 的版本
The source code for CVPR 2020 accepted paper "Recurrent Feature Reasoning for Image Inpainting"
The code of enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing
M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
Almost in every image processing or analysis work, image pre-preprocessing is crucial step. In medical image analysis, pre-processing is a very important step because the further success or performance of the algorithm mostly dependent on pre-processed image. In this lab, we are working with 3D Brain MRI data. In case of working with brain MRI removing the noise and bias field (which is due to inhomogeneity of the magnetic field) is very important part of preprocessing of brain MRI. To do so, we widely used algorithm Anisotropic diffusion, isotropic diffusion which can diffuse in any direction, and Multiplicative intrinsic component optimization (MICO) have been used for noise removal and bias field correction respectfully. Both quantitative and qualitative performance of the algorithms also have been analyzed.
A-Total-Variation-and-Group-Sparsity-Based-Tensor-Optimization-Model-for-Video-Rain-Streak-Removal
Multi-channels and Multi-models based Autoencoding Priors for Grayscale Image Restoration
PyTorch Implement of Context Encoders: Feature Learning by Inpainting
Code for the book Grokking Algorithms (https://amzn.to/29rVyHf)
《数据结构（Python语言描述）》"Fundamentals of Python:Data Structures" 电子书和配套代码
This is a Pytorch implementation for paper "High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis"
