Abstract
Low visibility in foggy days results in less contrasted and blurred images with color distortion which adversely affects and leads to the sub-optimal performances in image and video monitoring systems. The causes of foggy image degradation were explained in detail and the approaches of image enhancement and image restoration for defogging were introduced. The study proposed an enhanced and advanced form of the improved Retinex theory-based dehazing algorithm. The proposed algorithm achieved novel in the manner in which the dark channel prior was efficiently combined with the dark-channel prior into a single dehazing framework. The proposed approach performed the first stage in dehazing within the dark channel domain through implementation with an adaptive filter. This novel approach allowed for the dark channel features to be efficiently refined and boosted, a scheme, which according to the obtained results, significantly improved dehazing results in later stages. Experimental results showed that this approach did little to trade-off dehazing speed for efficiency. This makes the proposed algorithm a strong candidate for real-time systems due to its capability to realize efficient dehazing at considerably rapid speeds. Finally, experimental results were provided to validate the superior performance and efficiency of the proposed dehazing algorithm.
Highlights
This novel approach allowed for the dark channel features to be efficiently refined and boosted, a scheme, which according to the obtained results, significantly improved dehazing results in later stages
The core benefit of image dehazing lies in the manner in which it allows computer vision and human vision systems to capitalize on such improved and refined images for the realization of various applications
The metrics have reflected the true conditions of air in our daily lives and while we focus on the digital applications of images captured, we present these background theoretical components in order to enable an easy definition and understanding of the motivation of image dehazing and defogging
Summary
Most computer vision applications, ranging from low-level image analysis schemes to high-level object recognition, usually tend to assume the input image as the ultimate and most reliable source of the scene radiance. This, goes to establish that the performance of computer vision algorithms no matter how high level they may have a strong dependence upon the quality and reliability of the input image. Such algorithms will invariably suffer from biased and corrupted input images as a result of haze or fog presence within the target scene
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