Rain removal via shrinkage of sparse codes and learned rain dictionary

@article{Son2016RainRV,
  title={Rain removal via shrinkage of sparse codes and learned rain dictionary},
  author={Chang-Hwan Son and Xiao-Ping Zhang},
  journal={2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)},
  year={2016},
  pages={1-6}
}
Recently, sparse coding and dictionary learning have been widely used for feature learning and image processing. They can also be applied to the rain removal by learning two types of rain and non-rain dictionaries, and then forcing the sparse codes of the rain dictionary to be zero vectors. However, this approach can generate edge artifacts that appear in the non-rain regions, especially around the edges of objects. Based on this observation, a new approach of shrinking the sparse codes is… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 11 references

Visual Depth Guided Color Image Rain Streaks Removal Using Sparse Coding

IEEE Transactions on Circuits and Systems for Video Technology • 2014
View 5 Excerpts
Highly Influenced

Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition

IEEE Transactions on Image Processing • 2012
View 10 Excerpts
Highly Influenced

Structure extraction from texture via relative total variation

ACM Trans. Graph. • 2012
View 11 Excerpts
Highly Influenced

Histograms of oriented gradients for human detection

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) • 2005
View 5 Excerpts
Highly Influenced

Visibility in bad weather from a single image

2008 IEEE Conference on Computer Vision and Pattern Recognition • 2008
View 1 Excerpt

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