# SALT based Video Denoising
=============
SALT based Video Denoising accompanies the following publication: "Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising," IEEE International Conference on Computer Vision (ICCV), 2017. [ICCV 2017](http://openaccess.thecvf.com/ICCV2017.py), [PDF available](http://transformlearning.csl.illinois.edu/assets/Bihan/ConferencePapers/BihanICCV2017salt.pdf)
Description:
-----
We propose a video denoising method, based on a novel Sparse And Low-rank Tensor (SALT) model. An efficient and unsupervised
online unitary sparsifying transform learning method is introduced to impose adaptive sparsity on the fly. SALT based video denoising exhibits low latency and can potentially handle streaming videos. To the best of our knowledge, this is the first work that combines adaptive sparsity and low-rankness for video denoising, and the first work of solving the proposed problem in an online fashion.
The SALT package includes (1) a collection of the SALT Matlab functions, and (2) example data used in the SALT paper.
You can download our other software packages at: [Transform Learning Site](http://transformlearning.csl.illinois.edu/).
Paper
-----
Paper available [here](http://openaccess.thecvf.com/content_iccv_2017/html/Wen_Joint_Adaptive_Sparsity_ICCV_2017_paper.html).
In case of use, please cite our publication:
Bihan Wen, Yanjun Li, Luke Pfister, and Yoram Bresler, “Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising,” in Proc. IEEE Int. Conf. Computer Vision (ICCV), 2017.
Bibtex:
```
@InProceedings{Wen_2017_ICCV,
author = {Wen, Bihan and Li, Yanjun and Pfister, Luke and Bresler, Yoram},
title = {Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
```
Use
---
All codes are subject to copyright and may only be used for non-commercial research. In case of use, please cite our publication.
Contact Bihan Wen (bihan.wen.uiuc@gmail.com) for any questions.
Acknowledgement
---
The development of this software was supported in part by the National Science Foundation (NSF) under grants CCF-13-20953 and IIS 14-47879.
没有合适的资源?快使用搜索试试~ 我知道了~
【视频去噪】基于SALT实现视频去噪附Matlab代码.zip
共33个文件
m:24个
png:5个
md:1个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
5星 · 超过95%的资源 1 下载量 87 浏览量
2023-04-11
10:44:29
上传
评论
收藏 6.5MB ZIP 举报
温馨提示
1.版本:matlab2014/2019a,内含运行结果,不会运行可私信 2.领域:智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真,更多内容可点击博主头像 3.内容:标题所示,对于介绍可点击主页搜索博客 4.适合人群:本科,硕士等教研学习使用 5.博客介绍:热爱科研的Matlab仿真开发者,修心和技术同步精进,matlab项目合作可si信
资源推荐
资源详情
资源评论
收起资源包目录
【视频去噪】基于SALT实现视频去噪附Matlab代码.zip (33个子文件)
【视频去噪】基于SALT实现视频去噪附Matlab代码
Wen_Joint_Adaptive_Sparsity_ICCV_2017_paper.pdf 2.09MB
3.png 107KB
说明.txt 367B
demo_videodenoising.m 2KB
1.png 108KB
仿真咨询.png 350KB
更多代码关注我.png 114KB
demo_data
salesman.mat 4.03MB
salt_tool
sparse_l0.m 351B
module_TLapprox.m 3KB
onlineUTLupdate_analysis.m 2KB
module_vblockAggreagtion.m 2KB
module_vLRapprox.m 2KB
PSNR.m 140B
SALT_videodenoise_param.m 3KB
module_offlineBM.m 2KB
PSNR3D.m 172B
module_videoEnlarge.m 2KB
module_videoCrop.m 348B
module_videoBM_fix.m 3KB
module_video2patch.m 790B
4.png 107KB
vidosat_tool
getVIDOSAT_multipass_param.m 1KB
VIDOSAT_videodenoising_param.m 2KB
continuousOrder.m 141B
avi2grayVideo.m 2KB
PSNR.m 174B
HilbertCurve.m 633B
displayDictionaryElementsAsImage.m 3KB
PSNR3D.m 197B
README.md 2KB
VIDOSAT_videodenoising.m 11KB
SALT_videodenoising.m 6KB
共 33 条
- 1
资源评论
- hbtsm2023-11-15这个资源内容超赞,对我来说很有价值,很实用,感谢大佬分享~
天天Matlab科研工作室
- 粉丝: 3w+
- 资源: 7261
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功