Removing Rain from a Single Image via Discriminative Sparse Coding
South China University of Technology · National University of Singapore
Abstract
Visual distortions on images caused by bad weather conditions can have a negative impact on the performance of many outdoor vision systems. One often seen bad weather is rain which causes significant yet complex local intensity fluctuations in images. The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i.e. separate the rain layer and the de-rained image layer from an rainy image. Built upon a non-linear generative model of rainy image, namely screen blend mode, we proposed a dictionary learning based algorithm for single image de-raining. The basic idea is to sparsely approximate the patches of two layers by very high discriminative codes over a…
Citation impact
- FWCI
- 15.72
- Percentile
- 100%
- References
- 32
Authors
3Topics & keywords
- Discriminative model
- Computer science
- Image (mathematics)
- Artificial intelligence
- Coding (social sciences)
- Property (philosophy)
- Pattern recognition (psychology)
- Computer vision
- Reduced inequalities