Efficient Image Dehazing Algorithms with Conventional and Deep Learning Based Techniques Using Multi Spectral Images

Abstract

Image haze is a common problem in photography and imaging that occurs due to particles like dust, smoke, and moisture in the atmosphere. These particles scatter light, which leads to a reduction in visibility and a degradation in image quality. This scattering effect reduces contrast and visual clarity. The impact of haze is particularly significant in real-time applications, such as autonomous driving, surveillance, and aerial photography, where the quality of visual data is crucial for performance and accuracy. In autonomous driving, for instance, clear images are essential for the vehicle s vision systems to accurately detect and interpret the surrounding environment. Haze can obscure important details like road signs, pedestrians, and other vehicles, potentially leading to unsafe situations. Similarly, in surveillance, clear imagery is critical for the accurate identification of individuals and activities. Hazy images can hinder the effectiveness of these systems, compromising security and safety. To address these challenges, image dehazing techniques have been developed. These techniques aim to restore the clarity and contrast of hazy images, making them more useful for their intended applications. Image dehazing can be approached using conventional methods as well as deep learning methodologies. Conventional dehazing methods are often based on physical models of atmospheric scattering. However, conventional methods have limitations, particularly in handling complex and diverse haze conditions. This thesis mainly focus on the development of dehazing algorithms, both conventional and deep learning-based, has significantly advanced the field of image processing. These algorithms not only improve the visual quality of images but also enhance the performance of vision systems in various applications. By addressing issues related to low contrast and haze, these techniques contribute to safer autonomous driving, more effective surveillance, and higher-quality aerial photography.