The current challenges and prospects of rain detection and removal from videos

Rain removal from videos is among the key technologies in image processing and video surveillance because of the complex visual effects caused by rain. With the rapid development of computer vision technologies, rain removal has attracted increasing interests in both academic and industrial communities. In this paper, we firstly reviewed the main rain removal methods by classifying them into four categories based on the exploited rain properties. Some possible Challenges are also pointed out. And then, some constructive suggestions and prospects for the future research are brought forward.

[1]  Lei Wang,et al.  A Novel Nonlinear Regression Approach for Efficient and Accurate Image Matting , 2013, IEEE Signal Processing Letters.

[2]  Jing Xu,et al.  Removing rain and snow in a single image using guided filter , 2012, 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE).

[3]  Kwae-Hi Lee,et al.  Rain Removal Using Kalman Filter in Video , 2008, 2008 International Conference on Smart Manufacturing Application.

[4]  John P. Oakley,et al.  Low latency mitigation of rain induced noise in images , 2008 .

[5]  Sudipta Mukhopadhyay,et al.  Removal of rain from videos: a review , 2014, Signal Image Video Process..

[6]  Sudipta Mukhopadhyay,et al.  A Probabilistic Approach for Detection and Removal of Rain from Videos , 2011 .

[7]  Chenyuan Zhang,et al.  Motion robust rain detection and removal from videos , 2012, 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP).

[8]  Minmin Shen,et al.  A fast algorithm for rain detection and removal from videos , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[9]  Lei Wang,et al.  A Novel Recursive Bayesian Learning-Based Method for the Efficient and Accurate Segmentation of Video With Dynamic Background , 2012, IEEE Transactions on Image Processing.

[10]  Yu-Hsiang Fu,et al.  Single-frame-based rain removal via image decomposition , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Rajib Kumar Jha,et al.  Entropy-based rain detection and removal , 2013, 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE).

[12]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Hao Li,et al.  Rain Removal in Video by Combining Temporal and Chromatic Properties , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[14]  R. S. Anand,et al.  PSO-based learning of sub-band adaptive thresholding function for image denoising , 2012, Signal Image Video Process..

[15]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[16]  Mohinder Malhotra Single Image Haze Removal Using Dark Channel Prior , 2016 .

[17]  Shree K. Nayar,et al.  Detection and removal of rain from videos , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[18]  Ziyou Xiong,et al.  A two-step approach to see-through bad weather for surveillance video quality enhancement , 2011, 2011 IEEE International Conference on Robotics and Automation.

[19]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[20]  Ling Shao,et al.  A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior , 2015, IEEE Transactions on Image Processing.

[21]  Nasriah Zakaria,et al.  Review on raindrop detection and removal in weather degraded images , 2013, 2013 5th International Conference on Computer Science and Information Technology.

[22]  Chen Zhen,et al.  A New Algorithm of Rain (Snow) Removal in Video , 2013 .

[23]  Zhen Chen,et al.  A New Algorithm of Rain (Snow) Removal in Video , 2013, J. Multim..

[24]  Jie Chen,et al.  Rain removal from dynamic scene based on motion segmentation , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[25]  Takeo Kanade,et al.  Analysis of Rain and Snow in Frequency Space , 2008, International Journal of Computer Vision.

[26]  Shree K. Nayar,et al.  Vision and Rain , 2006 .

[27]  Jérémie Bossu,et al.  Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks , 2011, International Journal of Computer Vision.

[28]  Jing Xu,et al.  A Rain Removal Method Using Chromatic Property for Image Sequence , 2008 .

[29]  Ling Shao,et al.  Recursive Kernel Density Estimation for modeling the background and segmenting moving objects , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[30]  Ling Shao,et al.  Targeting Accurate Object Extraction From an Image: A Comprehensive Study of Natural Image Matting , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[31]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[32]  Li-Wei Kang,et al.  Self-Learning Based Image Decomposition With Applications to Single Image Denoising , 2014, IEEE Transactions on Multimedia.

[33]  Ling Shao,et al.  Single Image Dehazing Using Color Attenuation Prior , 2014, BMVC.

[34]  Guillermo Sapiro,et al.  Image inpainting , 2000, SIGGRAPH.

[35]  Nianjun Liu,et al.  Using the Shape Characteristics of Rain to Identify and Remove Rain from Video , 2008, SSPR/SPR.

[36]  D. Venkataraman,et al.  RESTORATION OF VIDEO BY REMOVING RAIN , 2012 .