Removing rain from a single image via Convolutional Neural Network

受恶劣天气的影响，室外视觉系统所获得的图像会劣化。雨是常见的恶劣天气之一，目前国内外关于去雨的问题已有一些解决的方案，但大多关注于视频去雨。由于该类方法是以丰富的时空相关信息为前提，因而并不适用于单幅图像去雨。近年来，单幅图像去雨的研究逐渐受到重视，然而现有方法需要在去雨效果和图像清晰度之间折中且计算效率低下，难以满足实际应用需求。 为此，本文针对单幅图像去雨，基于变分法和卷积神经网络提出三种新的单幅图像去雨算法，主要研究内容及成果如下： 1.提出基于梯度正则化的单幅图像去雨算法。首先设计一个引导平滑滤波器实现初步去雨。该滤波器在保证输出图像与输入图像一致性的前提下，引入梯度正则项，使其根...Affected by the bad weather, the images obtained by outdoor visual systems always degrade. Rain is one of the common bad weather. At home and abroad, there are some solutions about removal of rain, but most of it aims to videos. It can’t apply to single image since no temporal information can be obtained. Recently, the study of rain removal from a single image gradually receive more attention. Ho...学位：工学硕士院系专业：信息科学与技术学院_信号与信息处理学号：2332013115323

Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining

Single image deraining is a crucial problem because rain severely degenerates the visibility of images and affects the performance of computer vision tasks like outdoor surveillance systems and intelligent vehicles. In this paper, we propose the new convolutional neural network (CNN) called the wavelet channel attention module with a fusion network. Wavelet transform and the inverse wavelet transform are substituted for down-sampling and up-sampling so feature maps from the wavelet transform and convolutions contain different frequencies and scales. Furthermore, feature maps are integrated by channel attention. Our proposed network learns confidence maps of four sub-band images derived from the wavelet transform of the original images. Finally, the clear image can be well restored via the wavelet reconstruction and fusion of the low-frequency part and high-frequency parts. Several experimental results on synthetic and real images present that the proposed algorithm outperforms state-of-the-art methods.Comment: Accepted to IEEE ICIP 202

Self-Refining Deep Symmetry Enhanced Network for Rain Removal

Rain removal aims to remove the rain streaks on rain images. The state-of-the-art methods are mostly based on Convolutional Neural Network~(CNN). However, as CNN is not equivariant to object rotation, these methods are unsuitable for dealing with the tilted rain streaks. To tackle this problem, we propose Deep Symmetry Enhanced Network~(DSEN) that is able to explicitly extract the rotation equivariant features from rain images. In addition, we design a self-refining mechanism to remove the accumulated rain streaks in a coarse-to-fine manner. This mechanism reuses DSEN with a novel information link which passes the gradient flow to the higher stages. Extensive experiments on both synthetic and real-world rain images show that our self-refining DSEN yields the top performance.Comment: Accepted by ICIP 19. Corresponding and contact author: Hanrong Y