

浏览全部资源
扫码关注微信
1. 新疆大学信息科学与工程学院
2. 新疆维吾尔自治区信号检测与处理重点实验室
Published:2023
移动端阅览
[1]武锐,贾振红.基于多通道融合和组稀疏编码的视频去雪算法[J].新疆大学学报(自然科学版)(中英文),2023,40(01):69-78+86.
[1]武锐,贾振红.基于多通道融合和组稀疏编码的视频去雪算法[J].新疆大学学报(自然科学版)(中英文),2023,40(01):69-78+86. DOI: 10.13568/j.cnki.651094.651316.2022.02.07.0001.
DOI:10.13568/j.cnki.651094.651316.2022.02.07.0001.
大雪天气严重降低成像设备所采集视频的能见度,视频去雪算法可以恢复降雪视频的质量.为了去除降雪视频中的雪花,提出一种新的多通道融合和组稀疏编码去雪算法.针对视频的每一个色彩通道中存在的雪花成分进行去除,提出了一种全新的基于低秩矩阵分解的多通道融合背景建模方法,用于恢复干净的背景.为了检测雪花和运动前景,将运动成分中的雪花和运动前景分离以保留运动前景部分,提出了一种基于L0正则化的阈值化方法检测运动物体并分离雪花像素.最后,对被细小雪花遮挡的前景物体采用基于空间适应奇异值阈值的组稀疏编码进行去雪花处理,得到干净的前景.将干净的背景视频和干净的前景视频合成为一段完整的去雪后的视频.
The heavy snow weather severely reduces the visibility of the video captured by the imaging devices
and the video snow removal algorithm can restore the quality of the snow video. In order to remove the snowflakes in the snowfall video
a new method is proposed to remove snow based on multi-channel fusion and group sparse coding. This paper proposed to remove snowflake components in each color channel of the video
and a new multichannel fusion background modeling method based on low-rank matrix decomposition is proposed to restore a clean background. In order to detect snowflakes and motion foreground
the snowflakes and motion foreground in the motion component are separated to preserve the motion foreground component
and a thresholding method based on the L0 regularization term is proposed to detect moving objects and separate snowflake pixels. A spatially adaptive iterative singular-value thresholding-based group sparsity denoising method is utilized to remove the foreground occluded by small snowflakes to obtain clean foreground. A clean background video and a clean foreground video are combined into a complete snow-free video.
PEI S C, TSAI Y T, LEE C Y. Removing rain and snow in a single image using saturation and visibility features[C]//2014 IEEE International Conference on Multimedia and Expo Workshops(ICMEW). Chengdu:IEEE, 2014.
BARNUM P C, NARASIMHAN S, KANADE T. Analysis of rain and snow in frequency space[J]. International Journal of Computer Vision, 2010, 86(2/3):256-274.
SAKAINO H. A semitransparency-based optical-flow method with a point trajectory model for particle-like video[J]. IEEE Transactions on Image Processing, 2012, 21(2):441-450.
GARG K, NAYAR S K. When does a camera see rain?[C]//Tenth IEEE International Conference on Computer Vision(ICCV’05)Volume 1. Beijing:IEEE, 2005.
BOSSU J, HAUTI`ERE N, TAREL J P. Rain or snow detection in image sequences through use of a histogram of orientation of streaks[J]. International Journal of Computer Vision, 2011, 93(3):348-367.
KANG L W, LIN C W, FU Y H. Automatic single-image-based rain streaks removal via image decomposition[J]. IEEE Transactions on Image Processing, 2012, 21(4):1742-1755.
DONG H Y, ZHAO X J. Detection and removal of rain and snow from videos based on frame difference method[C]//The 27th Chinese Control and Decision Conference(2015 CCDC). Qingdao:IEEE, 2015.
KIM J H, SIM J Y, KIM C S. Video deraining and desnowing using temporal correlation and low-rank matrix completion[J].IEEE Transactions on Image Processing, 2015, 24(9):2658-2670.
杨燕妮,吴向前,刘鹏.基于帧间差分与码本模型的运动车辆检测算法[J].新疆大学学报(自然科学版), 2016, 33(2):203-208.
韩文轩,阿里甫·库尔班,黄梓桐.基于改进SSD算法的遥感影像小目标快速检测[J].新疆大学学报(自然科学版)(中英文), 2020,37(2):163-169.
LI M H, CAO X Y, ZHAO Q, et al. Online rain/snow removal from surveillance videos[J]. IEEE Transactions on Image Processing,2021, 30:2029-2044.
REN W H, TIAN J D, HAN Z, et al. Video desnowing and deraining based on matrix decomposition[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu:IEEE, 2017.
LIN X, MA L Z, SHENG B, et al. Utilizing two-phase processing with FBLS for single image deraining[J]. IEEE Transactions on Multimedia, 2021, 23:664-676.
WEI W, YI L X, XIE Q, et al. Should we encode rain streaks in video as deterministic or stochastic?[C]//2017 IEEE International Conference on Computer Vision(ICCV). Venice:IEEE, 2017.
HUANG S C, JAW D W, CHEN B H, et al. Single image snow removal using sparse representation and particle swarm optimizer[J].ACM Transactions on Intelligent Systems and Technology, 2020, 11(2):1-15.
CHEN W T, FANG H Y, DING J J, et al. JSTASR:joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal[C]//European Conference on Computer Vision(ECCV 2020). Cham:Springer International Publishing, 2020.
JAW D W, HUANG S C, KUO S Y. DesnowGAN:an efficient single image snow removal framework using cross-resolution lateral connection and GANs[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(4):1342-1350.
RECHT B, FAZEL M, PARRILO P A. Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization[J]. SIAM Review, 2010, 52(3):471-501.
BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2010, 3(1):1-122.
BOYKOV Y, VEKSLER O, ZABIH R. Fast approximate energy minimization via graph cuts[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision. Kerkyra:IEEE, 1999.
DONG W S, ZHANG L, SHI G M, et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2013, 22(4):1620-1630.
LIN T C, HOU L M, LIU H Q, et al. Reconstruction of single image from multiple blurry measured images[J]. IEEE Transactions on Image Processing, 2018, 27(6):2762-2776.
ZHA Z Y, YUAN X, WEN B H, et al. Group sparsity residual constraint with non-local priors for image restoration[J]. IEEE Transactions on Image Processing, 2020, 29:8960-8975.
XU J J, OSHER S. Iterative regularization and nonlinear inverse scale space applied to wavelet-based denoising[J]. IEEE Transactions on Image Processing, 2007, 16(2):534-544.
SHI Y, YANG X Y, GUO Y H. Translation invariant directional framelet transform combined with Gabor filters for image denoising[J]. IEEE Transactions on Image Processing, 2014, 23(1):45-55.
WANG Y, JODOIN P M, PORIKLI F, et al. CDnet 2014:an expanded change detection benchmark dataset[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus:IEEE, 2014.
MAZUMDER R, HASTIE T, TIBSHIRANI R. Spectral regularization algorithms for learning large incomplete matrices[J]. Journal of Machine Learning Research, 2010, 11(11):2287-2322.
LI C L, WANG X, ZHANG L, et al. Weighted low-rank decomposition for robust grayscale-thermal foreground detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(4):725-738.
PANG Y W, YE L, LI X L, et al. Incremental learning with saliency map for moving object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(3):640-651.
HUYNH-THU Q, GHANBARI M. Scope of validity of PSNR in image/video quality assessment[J]. Electronics Letters, 2008,44(13):800-801.
WANG S Q, MA K D, YEGANEH H, et al. A patch-structure representation method for quality assessment of contrast changed images[J]. IEEE Signal Processing Letters, 2015, 22(12):2387-2390.
ZHANG L, ZHANG L, MOU X Q, et al. FSIM:a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8):2378-2386.
0
Views
122
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621