Rain-streaks Detection and Removal In Single Image Using Curvelet Transform

A. A. Bahashwan, P. Ehkan, Syed Alwee Aljunid Syed Junid, A. Safar, Mazen Abdullah Bahashwan, Adel Hafeezallah
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Abstract

This study implements a new way to address the issue of rain streaks detection and elimination from a single picture based on the transform of Curvelet. This approach depends on a decomposing of the rainy image into different scales and sub-bands frequencies by using the curvelet transform. Features have been extracted from each sub-band frequency and the neural network will classify these features into “rain” or “non-rain” signatures. The reconstructed image is obtained without the sub-bands that have the rain signature. The findings from the experiments indicate that the proposed approach improves the visualizing quality as well as PSNR and outperforms previous rain removal algorithms.
基于曲线变换的单幅图像雨纹检测与去除
本研究实现了一种基于Curvelet变换的单幅图像雨纹检测与消除的新方法。该方法依赖于利用曲线变换将降雨图像分解成不同的尺度和子频带频率。从每个子带频率中提取特征,神经网络将这些特征分类为“下雨”或“不下雨”特征。重建后的图像去掉了具有雨特征的子带。实验结果表明,该方法提高了可视化质量和PSNR,优于以往的除雨算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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