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Images taken on rainy days have rain streaks of varying degrees of intensity, which seriously affect the visibility of the background scene. Aiming at the above problems, we propose a rain mark removal algorithm based on the combination of dual-attention mechanism U-Net and multi-convolution. First, we add a double attention mechanism to the encoder of U-Net. It can give different weights to the rain mark features that need to be extracted in different channels and spaces so that sufficient rain mark features can be obtained. With different dilation factors, we can obtain rain mark characteristics of different depths. Secondly, the multi-convolutional channel integrates the characteristics of rain streaks and prepares sufficient rain mark information for the task of clearing rain streaks. By introducing a cyclic rain streaks detection and removal mechanism into the network architecture, it can achieve gradual removal of rain streaks. Even in the case of heavy rain, our algorithm can get good results. Finally, we tested on both synthetic and real datasets to obtain subjective results and objective evaluations. Experimental results show that for the rainy day image de-rain task with different intensities of rain streaks, our algorithm is more robust. Moreover, the ability of our algorithm to remove rain streaks is better than that of the other five different classical algorithms. The de-raining images produced by our algorithm are visually sharper, and its visibility enhancements are effective for computer vision applications (Google Vision API).

Rain can have a detrimental effect on optical components, leading to the appearance of streaks and halos in images captured during rainy conditions. These visual distortions caused by rain and mist contribute significant noise information that can compromise image quality. In this paper, we propose a novel approach for simultaneously removing both streaks and halos from the image to produce clear results. First, based on the principle of atmospheric scattering, a rain and mist model is proposed to initially remove the streaks and halos from the image by reconstructing the image. The Deep Memory Block (DMB) selectively extracts the rain layer transfer spectrum and the mist layer transfer spectrum from the rainy image to separate these layers. Then, the Multi-scale Convolution Block (MCB) receives the reconstructed images and extracts both structural and detailed features to enhance the overall accuracy and robustness of the model. Ultimately, extensive results demonstrate that our proposed model JDDN (Joint De-rain and De-mist Network) outperforms current state-of-the-art deep learning methods on synthetic datasets as well as real-world datasets, with an average improvement of 0.29 dB on the heavy-rainy-image dataset.

Visibility restoration of color rainy images is an inevitable task for the researchers in many vision based applications. Rain produces a visual impact onimage, so that the intensity and visibility of image is low. Therefore, there is a need to develop a robust visibility restoration algorithm for the rainy images. Inthis paper we proposed a robust visibility restoration framework for the images captured in rainy weather. The framework is the combined form of convolutionneural network for rain removal and low light image enhancement for low contrast. The output results of the proposed framework and other latest de-rainyalgorithms are estimated in terms of PSNR, SSIM and UIQI on rainy image from different databases. The quantitative and qualitative results of the proposedframework are better than other de-rainy algorithms. Finally, the obtained visualization result also shows the efficiency of the proposed framework.