low code HD - Moozonian

GitHub Repo https://github.com/AlirezaOmrani95/HDR-Imaging

AlirezaOmrani95/HDR-Imaging

This project is about producing a tone-mapped version of High Dynamic Range (HDR) image from three Low Dynamic Range (LDR) images. An autoencoder with three encoders and one decoder were used in this project. Additionally, this code is an implementation of the article called Deep Autoencoder Multi-Exposure HDR Imaging, which can be found on the following DOI:

GitHub Repo https://github.com/mtntruong/LRT-HDR

mtntruong/LRT-HDR

Python code and data for "Deep Unrolled Low-Rank Tensor Completion for High Dynamic Range Imaging", IEEE TIP 2022

GitHub Repo https://github.com/Sagittarius-B-Astro/BURST-CV

Sagittarius-B-Astro/BURST-CV

Temporary repository for storing code used for computer vision implementation of Blue Robotics Low-Light HD USB Camera

GitHub Repo https://github.com/SOBIKA-S-K/Existing-I2C-HDL-Code-2

SOBIKA-S-K/Existing-I2C-HDL-Code-2

I²C (Inter-Integrated Circuit) was developed by Philips Semiconductor (now NXP Semiconductors) in 1982. It is a synchronous, multi-master, multi-slave serial communication protocol designed for low-speed communication between integrated circuits. Only 2 wires (SCL and SDA) are required for communication, which makes design simpler.

GitHub Repo https://github.com/X-laboratory-678/HDT_2026

X-laboratory-678/HDT_2026

Data and code for the manuscript "Corridor optimization enables cost-competitive and low-emission battery electric long-haul road freight".

GitHub Repo https://github.com/Hazey-PG/hd-progressbar

Hazey-PG/hd-progressbar

Updated UI, Low Resmon, Updated Code

GitHub Repo https://github.com/LinhzLab/HDSL

LinhzLab/HDSL

The R code for paper `Low-rank assisted high-dimensional subgroup learning'.

GitHub Repo https://github.com/zhiqinzhu123/HDR-Multi-exposure-image-Fusion-Source-Code-

zhiqinzhu123/HDR-Multi-exposure-image-Fusion-Source-Code-

Matlab Source Code of article “ A Precise Multi-Exposure Image Fusion Method Based on Low-level Features” research use only. Please cite： Qi, G.; Chang, L.; Luo, Y.; Chen, Y.; Zhu, Z.; Wang, S. A Precise Multi-Exposure Image Fusion Method Based on Low-level Features. Sensors 2020, 20, 1597. BIBTEX: @Article{s20061597, AUTHOR = {Qi, Guanqiu and Chang, Liang and Luo, Yaqin and Chen, Yinong and Zhu, Zhiqin and Wang, Shujuan}, TITLE = {A Precise Multi-Exposure Image Fusion Method Based on Low-level Features}, JOURNAL = {Sensors}, VOLUME = {20}, YEAR = {2020}, NUMBER = {6}, ARTICLE-NUMBER = {1597} }

GitHub Repo https://github.com/Goluck-Konuko/hdac

Goluck-Konuko/hdac

H-DAC: Hybrid coding with Deep Animation Models for Ultra-Low Bitrate Video Conferencing

GitHub Repo https://github.com/ramgoenka/power-analysis-two-sample-mean-tests-HDLSS-settings

ramgoenka/power-analysis-two-sample-mean-tests-HDLSS-settings

This repository contains all the code written for the paper "Power Analysis of Two-Sample Mean Tests in High-Dimension, Low-Sample-Size (HDLSS) settings".