Image Dehazing and Rain Removal Using Digital Image and Video Processing
Date Issued
2012
Date
2012
Author(s)
Lee, Tzu-Yen
Abstract
Poor visibility in bad weather is a major problem for many applications of computer vision such as surveillance, intelligent vehicles, and outdoor object recognition, etc…. The reason is that the substantial presence of atmospheric particles has significant size and distribution in the participating medium. Based on this, weather conditions can be characterized as static and dynamic cases. Specifically, static bad weather such as fog and haze caused by microscopic particles are usually spatially and temporally consistent. Oppositely, dynamic bad weather has large particles such as raindrops and snowflakes. Because spatially and temporally neighboring areas are affected by rain and snow differently, the analysis is more difficult. Under these conditions, the human viewer would be annoyed and confused. They also degrade the effectiveness of any computer vision algorithm based on small features. Therefore, it is necessary to model the visual effects for the various cases and then remove them.
In this thesis, we introduce three existing typical single image dehazing methods: contrast-based [1], independent component analysis [2], and dark channel prior-based [3]. To improve the dehazing quality, we propose a robust and effective dehazing method. Unlike other existing methods, our method gives satisfactory dehazing quality during daytime and nighttime. Besides, three existing typical rain removal methods: streak-based detection [4], image-based blurring [5], and frequency-based analysis [6] are also introduced in the literature. In this follows, we design a simple but effective rain removal method by combining the average-based rain detection and block-based rain removing procedures.
Subjects
Dehazing
Rain removal
Image Restoration
video Restoration
Image Enhancement
Type
thesis
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