Application of Daubechies Wavelet Transformation for Noise Rain Reduction on the Video

Currently, the use of digital video in the field of computer science is increasingly widespread, such as the process of tracking objects, the calculation of the number of vehicles, the classification of vehicle types, vehicle speed estimation and so forth. The process of taking digital video is often influenced by bad weather, such rain. Rain in digital video is considered noise because it is able to block objects being observed. Therefore, a rainfall noise reduction process is required in the video. In this study, the reduction of rain noise in digital video is using Daubechies wavelet transformation through several processes, namely: wavelet decomposition, fusion process, thresholding process and reconstruction process. The threshold value used in the thresholding process is VishuShrink, BayesShrink, and NormalShrink. The result of the implementation and noise reduction test show that Daubechies db2 level 3 filter gives the result with the biggest PSNR value. As for the type of threshold that provides optimal results is VishuShrink

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

Detection of Unfocused Raindrops on a Windscreen using Low Level Image Processing

International audienceIn a scene, rain produces a complex set of visual effects. Obviously, such effects may infer failures in outdoor vision-based systems which could have important side-effects in terms of security applications. For the sake of these applications, rain detection would be useful to adjust their reliability. In this paper, we introduce the problem (almost unprecedented) of unfocused raindrops. Then, we present a first approach to detect these unfocused raindrops on a transparent screen using a spatio-temporal approach to achieve detection in real-time. We successfully tested our algorithm for Intelligent Transport System (ITS) using an on-board camera and thus, detected the raindrops on the windscreen. Our algorithm differs from the others in that we do not need the focus to be set on the windscreen. Therefore, it means that our algorithm may run on the same camera sensor as the other vision-based algorithms

Deraining and Desnowing Algorithm on Adaptive Tolerance and Dual-tree Complex Wavelet Fusion

Severe weather conditions such as rain and snow often reduce the visual perception quality of the video image system, the traditional methods of deraining and desnowing usually rarely consider adaptive parameters. In order to enhance the effect of video deraining and desnowing, this paper proposes a video deraining and desnowing algorithm based on adaptive tolerance and dual-tree complex wavelet. This algorithm can be widely used in security surveillance, military defense, biological monitoring, remote sensing and other fields. First, this paper introduces the main work of the adaptive tolerance method for the video of dynamic scenes. Second, the algorithm of dual-tree complex wavelet fusion is analyzed and introduced. Using principal component analysis fusion rules to process low-frequency sub-bands, the fusion rule of local energy matching is used to process the high-frequency sub-bands. Finally, this paper used various rain and snow videos to verify the validity and superiority of image reconstruction. Experimental results show that the algorithm has achieved good results in improving the image clarity and restoring the image details obscured by raindrops and snows