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Image and Video De-hazing Based on an Improved MSR Algorithm

Author: LiuMing
Tutor: XuChao
School: Anhui University
Course: Microelectronics and Solid State Electronics
Keywords: image de-hazing MSR video de-hazing motion estimation spatiotemporal coherence
CLC: TP391.41
Type: Master's thesis
Year: 2014
Downloads: 3
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Abstract


With the rapid development of computer vision technology, imaging system and video monitoring system can capture image information clearly and richly. Video surveillance systems such as traffic monitoring system, can get accurate and clear characteristics of the vehicle license plate number in the sunny weather conditions. These images and video data can be used as vehicle illegal evidence. But In the rainning or foggy weather conditions, images or videos captured by these outdoor monitoring systems will be degraded. Those image or video has low contrast and color distortion, If left untreated, these data will lose its original value. In this context, this paper studies defogging treatment for a single image and video sequences.This paper mainly consists of two aspects:Firstly, study of defogging treatment for a single image:Based on the traditional MSR(multi-scale Retinex) algorithm, the traditional defogging treatment algorithms exist color distortion and halo phenomenon. Based on the traditional MSR algorithm, we adopted two weight factors to improve the problem of over enhancement. Through comparative experiment; in addition we improved by proposing adaptive filter kernel function. In the modified algorithm, an adaptive anisotropic Gaussian filtering method is introduced, and the orientation of the long axes is determined according to the gradient orientation in the position, we can intuitive felt the image contrast is enhanced and the visual effect is improved, the stripe detail is more obvious. The method proposed in this paper avoid excessive enhancement and solves the problems which easily appeared halo phenomenon and the quality of image analysis to quantify the conclusion. Furthermore, comparing the quantitative data through image quality analysis, we can further verify the effectiveness of the proposed algorithm.Secondly, study of defogging treatment for video sequences:(1) According to a certain frequency, we sample some key frames in the video, enhancing the key frames by the improved MSR algorithm, we can obtain clearer de-hazing images of key frame.(2)Adopting bidirectional LK optical flow method, estimating motion displacement point of pixel target, obtain the diffusion image of the non key frames (transform the defogging key frame image to gray image and then reverse it). After reversing and recovering of the non key frame diffusion image,the non key frame defogging images can be obtained.(3) Synthesizing the key frames defogging images and the non key frames defogging images, we can obtained the complete defogging video. Additionally, correcting the defogging video by time-space consistency, removing local jitter or jump problem, and the correctness and effectiveness of the proposed algorithm is verified through experiment.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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