Lu Li 's repositories
awesome-low-light-image-enhancement
This is a resouce list for low light image enhancement
MegSpot
MegSpot是一款高效、专业、跨平台的图片&视频对比应用
Retinexformer
"Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement" (ICCV 2023)
Awesome-Image-Quality-Assessment
A comprehensive collection of IQA papers
ISP-pipeline-hdrplus
Denoise,HDR,Isppipeline,Image-processing(图形处理),camera, Isp ,HDRplus
leetcode
Python & JAVA Solutions for Leetcode
lilubit
Config files for my GitHub profile.
openISP
Image Signal Processor
bank_interview
:bank: 银行笔试面试经验分享及资料分享（help you pass the bank interview, and get a amazing bank offer!）
zynq_cam_isp_demo
基于verilog实现了ISP图像处理IP
leetcode_101
LeetCode 101：和你一起你轻松刷题（C++）
ECLNet
ACMMM 2022 (Official Implementation of "Exposure-Consistency Representation Learning for Exposure Correction")
contrastive-alternative-learning
The official code of the paper "Best of Both Worlds: See and Understand Clearly in the Dark".
MF-LIME
基于Retinex模型和多尺度融合的低光照图像增强技术 Low-light image enhancement technology based on Retinex model and multi-scale fusion
flatnet
This is the official pytorch code repo for the ICCV 2019 paper Towards Photorealistic Reconstruction of Highly Multiplexed Lensless Images and the TPAMI 2020 paper FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements
Matlab-Image-Denoise
Matlab-Image-Denoise
HEP
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”
U-shape_Transformer_for_Underwater_Image_Enhancement
U-shape Transformer for Underwater Image Enhancement
CV_Interview
I hope this repo can help you a lot!
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++
blind_iqa_contrast
This repo compiles various blind image quality acessment methods focused on contrast evaluation. Only code that works in Python or Octave.
CEIQ
:art: Code for "No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement" by J. Yan, J. Li, X. Fu