Single Image Dehazing, Rain/Snow Removal and Underwater Enhancement Using Digital Image Processing
Date Issued
2014
Date
2014
Author(s)
Tsai, Yu-Tai
Abstract
Poor visibility in bad environment 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 steady and dynamic cases. Specifically, steady bad weather such as fog and haze caused by microscopic particles is usually spatially and temporally consistent. Oppositely, dynamic bad weather such as rain and snow in made up of large particles. Because spatially and temporally neighboring areas are affected by rain and snow differently, the analysis is more difficult. However, the poor visibility in underwater photography is caused by light scattering and color shift. Color shift corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by bluish tone. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Under these conditions, the human viewer would be annoyed. They also degrade the effectiveness of any computer vision algorithm based on small features and varying degrees of attenuation. 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, independent component analysis, and dark channel prior-based. To improve the dehazing quality, we propose a robust and effective dehazing method. Unlike other existing methods, there is the satisfactory dehazing quality during daytime and nighttime by our methods. And then, four existing typical rain and snow removal methods in single image: guidance image based image decomposition analysis, adaptive nonlocal means filter, and frequency-based analysis are also introduced in the literature. In this follows, we design a simple but effective method divide the rain or snow removal scheme into two parts, the first part is detection of rain or snow and the second part is inpainting. Besides, three existing typical underwater enhanced methods: histogram-based equalization, wavelength-based compensation, and fusion based are also introduced in the literature. In this follows, we design a simple but effective underwater enhanced method, and its main idea is combining the color correction, contrast stretching, and histogram equalization. Unlike other existing methods, we’ll get a better result which takes less processing time and highly enhances visibility and superior color fidelity by our method. We believe that we’ll run real-time on hardware in optimized circumstances.
Subjects
除霧
除霾
除雨
除雪
水下影像
影像復原
視頻復原
影像增強
Type
thesis
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