An Image Rain Removal algorithm based on the depth of field and sparse coding

Rainfall weather can always seriously deteriorate the quality of the outdoor monitoring system image. Since the decomposition based methods do not need to impose any restrictions on the types of rain, they have a wider application in removing the rain streaks. However, they still have the problems of rain residues in the low frequency component, and mismatching the background and the rain streaks with the same gradient in the high frequency. In this condition, we propose an image rain removal algorithm based on the depth of field and sparse coding. The algorithm includes four steps: image decomposition, dictionary learning, atomic clustering based on Principal Component Analysis and Support Vector Machine, image revising based on the depth of field saliency map. Firstly, the image is decomposed by using the combination of bilateral filtering and short-time Fourier transform, so that the contour in the low-frequency part of the image can be better preserved. The depth of field saliency map of the image is utilized to eliminate the rain residues in the low frequency components, and also to solve the problem of mis-matching the background and the rain streaks with the same gradient in the high frequency components. The experimental results demonstrate that the proposed algorithm performs better both in rain removal and preserving the detailed information of the image than current methods.