Abstract
This paper proposes a single image haze removal algorithm that shows a marked improvement on the color attenuation prior-based method. Through a vast number of experiments on a wide variety of images, it is discovered that there are problems in the color attenuation prior, such as color distortion and background noise, which arise due to the fact that the priors do not hold true in all circumstances. Successful resolution of these problems using the proposed algorithm shows its superior performance to other state-of-the-art methods in terms of both subjective visual quality and quantitative metrics, on both synthetic and natural hazy image datasets. The proposed algorithm also is computationally friendly, due to the use of an efficient quad-decomposition algorithm for atmospheric light estimation and a simple modified hybrid median filter for depth map refinement.
Highlights
The prevalence of consumer cameras and the urgent need for groundbreaking work in surveillance or autonomous driving systems have demanded the rapid development of image/video processing algorithms
The use of a minimum filter is beneficial to the estimation of atmospheric light, but it creates the unfortunate byproduct of blurring the depth map; a very large guided image filter must be used, and therein causes Color Attenuation Prior (CAP)’s high computational cost
By carefully examining the advantages and disadvantages of CAP, as well as other state-of-the-art haze removal algorithms, Improved Color Attenuation Prior (ICAP) is proposed to overcome some of the existing limitations
Summary
The prevalence of consumer cameras and the urgent need for groundbreaking work in surveillance or autonomous driving systems have demanded the rapid development of image/video processing algorithms. The task of eliminating unwanted-but-inevitable weather phenomena from an image has drawn increasing attention in recent years This image processing problem is known as visibility restoration. Recent efforts largely have led to single-image dehazing using two-dimensional images without any external knowledge Such an algorithm, needs to impose priors on the recovered image, for example, enhanced contrast or less attenuated color [23]. The haze removal method presented in this paper is an improvement upon CAP in terms of both computational efficiency and the dehazed image’s quality. This is achieved by examining the existing drawbacks of CAP, and proposing effective solutions for individual ones.
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