Image rain removal algorithm based on multi-cascade progressive convolution structure

ZHANG Yong; GUO Jie-long; WANG Fan; LAN Hai; YU Hui; WEI Xian

doi:10.37188/CJLCD.2022-0383

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Image rain removal algorithm based on multi-cascade progressive convolution structure

Image Processing | 更新时间：2023-10-10

- Image rain removal algorithm based on multi-cascade progressive convolution structure
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 10, Pages: 1409-1422(2023)
- 作者机构：
  
  1.福州大学电气工程与自动化学院，福建福州 350108
  2.中国科学院福建物质结构研究所，福建福州 350108
- 作者简介：
- 基金信息：
  
  Fujian Science and Technology Plan(2021T3003);Quanzhou Science and Technology Plan(2021C065L)
- DOI：10.37188/CJLCD.2022-0383
  CLC：
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ZHANG Yong, GUO Jie-long, WANG Fan, et al. Image rain removal algorithm based on multi-cascade progressive convolution structure. [J]. Chinese Journal of Liquid Crystals and Displays 38(10):1409-1422(2023)
DOI：

ZHANG Yong, GUO Jie-long, WANG Fan, et al. Image rain removal algorithm based on multi-cascade progressive convolution structure. [J]. Chinese Journal of Liquid Crystals and Displays 38(10):1409-1422(2023) DOI： 10.37188/CJLCD.2022-0383.

摘要

雨天图像会影响计算机视觉任务的效果与精度。雨天图像常常包含来自不同方向、大小、形状的雨点或雨痕，在对这些雨点、雨痕进行去除时，现有的方法往往没有考虑到雨天图像不同精细尺度下的特征信息，仅采用单一尺度进行图像去雨存在很大缺陷，无法恢复出足够清晰的视觉任务图像。受益于卷积神经网络架构的强大特征提取能力，本文提出了一种端到端的多级联递进卷积结构算子，该算子包含4层卷积层，通过阶梯化连接构成一个整体模块，该模块可以针对多尺度场景下的雨天进行特征提取并整合。将该算子模块嵌入到渐进循环网络结构中，利用循环结构多次去除雨纹，最终有效还原出接近真实图像的无雨图像。该方法在现有的人工合成雨图数据集Rain100H、Rain100L、Rain800与自动驾驶领域合成雨图数据集BDD1000上进行了对比实验。实验结果表明，该算法在4个数据集上的PSNR值达到了30.70，37.91，27.63，35.74 dB，SSIM值达到了0.914，0.980，0.894，0.977。通过真实雨图数据集去雨结果的可视化展示，充分验证了本文方法在去雨任务上的有效性。

Abstract

Rainy images can affect the performance and accuracy of computer vision tasks. Rainy images often contain raindrops or rain marks from different directions， sizes， and shapes. When removing these raindrops and rain marks， the existing methods often do not take into account the feature information of rainy images at different fine scales， and only use a single scale. There is a big defect in image deraining， and it is impossible to restore a clear enough image for visual tasks. Therefore， benefiting from the powerful feature extraction capability of the convolutional neural network architecture， an end-to-end multi-cascade progressive convolution structure operator is proposed， which consists of four convolutional layers connected through a ladder to form an overall module. This module can extract and integrate rainy weather features in multi-scale scenes. The operator module is embedded into the progressive recurrent network structure， the recurrent structure is used to remove rain streaks many times， and finally the rain-free image close to the real image is effectively restored. The method is compared with the existing artificially synthesized rain image datasets Rain100H， Rain100L， Rain800 and the synthetic rain image dataset BDD1000 in the field of automatic driving. The experiment results shows that the PSNR values of the algorithm on the four datasets reach 30.70 ， 37.91， 27.63， 35.74 dB， and the SSIM values reach 0.914， 0.980， 0.894， 0.977. Through the visual display of the rain removal results of the real rain map dataset， the effectiveness of the method in this paper on the rain removal task is fully verified.

关键词

图像去雨多级联递进卷积结构卷积神经网络深度学习多尺度特征残差结构

Keywords

image rain removalmulti-cascade progressive convolution structureconvolutional neural networkdeep learningmulti-scale featureresidual structure

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