# A Single Image Enhancement Technique Using Dark Channel Prior
###### Abstract  
In this paper, we propose a novel single image enhancement technique for defogging by using dark channel prior. The traditional dark channel prior methods for defogging have problems of high time complexity, edge effect, and failure of dark channel prior. To overcome the problems of high time complexity and edge effect, firstly, a four-point weighting algorithm is proposed to estimate the atmospheric light value accurately, and the dark channel prior is used to estimate the rough transmittance. Then, the gray-scale image of the input image is used to refine the transmittance. After that, an atmospheric scattering model is designed to restore the fog-free image. To solve the problem that the dark channel prior can not process the high brightness area, a combination of edge detection and maximum inter-class variance is used to segment the sky area and non-sky area. Finally, the improved defogging method is used for processing the non-sky area, and the enhancement algorithm via sequential decomposition is used for handling the sky area. Extensive experiments show that the improved algorithm can not only reduce the time complexity, but also effectively improve the edge effect. At the same time, it can also solve the problem of failure of dark channel prior. dark channel prior; edge effect; four-point weighting; segmentation Article  
A Single Image Enhancement Technique Using Dark Channel Prior  
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areas of fogging images are easily over-enhanced. In that case, the overall visual effect after defogging looks very unnatural. Defogging methods based on image restoration can overcome the above disadvantages of enhancement technology-based methods. The authors of these methods conducted in-depth research on the specific foggy images, and proposed a series of image defogging algorithms based on atmospheric scattering models. According to the difference of additional information in the imaging scene, image restoration methods can be roughly divided into two types. In the first category, they first need to collect different weather images in the same scene, and then use these images as inputs [7] to implement their methods. The principle of these methods for image restoration is the differences between the input images. These methods have satisfactory defogging effect, but they need to acquire images under different weather, which results in poor real-time performance. In the second category, they take a fogging image as input, and prior knowledge or assumptions are used to perform image restoration. Literature [8] proposes an image defogging algorithm based on the prior condition, which is simple and fast, but local areas of restored images are too saturated. Literature [9] estimates the transmittance by assuming that the local transmittance is locally uncorrelated with the shading area in the scene. This algorithm has a better defogging effect on thin fog, but distortion will occur in dense fog areas. [PERSON] [10] assumes that the atmospheric dissipation function tends to the maximum value in a certain area, and then uses the median filtering to estimate its value. However, the median filter has a weak defogging ability in the areas of abrupt depth. Aiming at overcoming the shortcomings of the above defogging algorithms, [PERSON] [11] proposes a defogging algorithm based on the dark channel prior. This algorithm estimates the transmittance through the prior knowledge of dark channel, and uses soft matting technology to optimize the transmittance. It can achieve a good defogging effect for most natural images. However, the introduction of soft matting technology will increase the time complexity. At the same time, the algorithm is particularly prone to distortion when processing sky areas. Based on these shortcomings, a large number of researchers proposed a series of improved methods based on the dark channel prior.