Source Code Collection for Reproducible Research

本网站致力于分享计算科学领域(包括信号处理、计算机视觉、机器学习和神经计算)的最新研究成果的源代码,旨在提升科学研究的可复现性。网站提供了包括图像去噪、图像去马赛克、图像插值与超分辨率、图像分割与解析、盲源分离、图像注册、压缩感知等领域的专业资源。

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Source Code Collection for Reproducible Research

【 http://www.csee.wvu.edu/~xinl/source.html】

“It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong” - Richard Feynman

 

"As a method for finding things out, science lives by its disdain for authority and its reliance on experimentation." - Chris Quigg

 

Welcome on this site about reproducible research in computational science (including signal processing, computer vision, machine learning and neural computation). This site is intended to share the source codes of the latest advances in various technical fields to the best of my knowledge. Only through Reproducible Research (RR), can we live up to the standard that hard-core science has established since Bacon and Newton. If you know of any release of the source codes that is missing from the list or any broken link, please kindly let me know.

Image denoising

Image coding

Image demosaicing

Image interpolation and Superresolution

Image segmentation/parsing and matting

Stereo matching& SfM

Image deblurring

Blind image deblurring

 

Image inpainting/Texture synthesis

PDE-based image processing

Image quality assessment

Biometrics

 

HDR imaging

Gradient-domain image processing

Video coding

Texture classification

Object recognition

 

Blind source separation

Image registration

Visual tracking

 

Manifold learning and embedding

Wavelets and frames

Compressed Sensing

Evolutionary computing

Networking Research

Biomedical Imaging

Data Clustering

Sampling&Simulation

 

Graphics, Cartoons&Motion

 

Machine learning&Neural Networks

Miscellaneous

Links to other communities' reproducible research effort

Links to reproducible books/journals/tutorials

Links to other individual's reproducible research


   Google Scholar is great but if most papers in computational sciences could be accessed along with their source codes (not just the citation number), the world for scientific researchers will be even better. It is easy to find papers these days but when can finding the source codes of a paper become easy too? I think Don Knuth's old-day advices on Literate Programming are still relevant to the current state of reproducible research. I believe that the time is ripe for significantly promoting experimentally reproducible research (just like mathemathetical theories - mentally reproducible research), and that we can best achieve this by considering research codes to be works of literature (so they can be easily picked up by other researchers). Only when the reproducibility of research in computational science becomes a default instead of a luxury, can we look further by standing on each other's shoulders.

一个好的资源整合网址:

http://www.csee.wvu.edu/~xinl/source.html

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