A Shearlets-Based Method for Rain Removal from Single Images

Guomin Sun,Jinsong Leng,Carlo Cattani

doi:10.3390/app9235137

Guomin Sun, Jinsong Leng + Show 1 more

Open Access

https://doi.org/10.3390/app9235137

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Abstract

This work focuses on the problem of rain removal from a single image. The directional multilevel system, Shearlets, is used to describe the intrinsic directional and structure sparse priors of rain streaks and the background layer. In this paper, a Shearlets-based convex rain removal model is proposed, which involves three sparse regularizers: including the sparse regularizer of rain streaks and two sparse regularizers of the Shearlets transform of background layer in the rain drops’ direction and the Shearlets transform of rain streaks in the perpendicular direction. The split Bregman algorithm is utilized to solve the proposed convex optimization model, which ensures the global optimal solution. Comparison tests with three state-of-the-art methods are implemented on synthetic and real rainy images, which suggests that the proposed method is efficient both in rain removal and details preservation of the background layer.

Highlights

IntroductionThe outdoor images are often degraded by the rain streak and other bad weather conditions, and these bad weather conditions can lead to the change of local or global intensities and color contrast in real images, causing unclear visible scenes
Rain removal from a single image is an important issue in processing outdoor vision problems.the outdoor images are often degraded by the rain streak and other bad weather conditions, and these bad weather conditions can lead to the change of local or global intensities and color contrast in real images, causing unclear visible scenes
This subsection tests the performance of different methods under real rainy images

Summary

Introduction

The outdoor images are often degraded by the rain streak and other bad weather conditions, and these bad weather conditions can lead to the change of local or global intensities and color contrast in real images, causing unclear visible scenes. Such degradation severely affects the performance of algorithms in computer vision systems. In [4], Garg and Nayar found that the video rain visibility relies heavily on the exposure time and the depth of field, based on this fact, they introduced a self-adaption parameter model to remove rain streaks efficiently.

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