Variational Histogram Equalization for Single Color Image Defogging

Abstract

Foggy images taken in the bad weather inevitably suffer from contrast loss and color distortion. Existing defogging methods merely resort to digging out an accurate scene transmission in ignorance of their unpleasing distortion and high complexity. Different from previous works, we propose a simple but powerful method based on histogram equalization and the physical degradation model. By revising two constraints in a variational histogram equalization framework, the intensity component of a fog-free image can be estimated in HSI color space, since the airlight is inferred through a color attenuation prior in advance. To cut down the time consumption, a general variation filter is proposed to obtain a numerical solution from the revised framework. After getting the estimated intensity component, it is easy to infer the saturation component from the physical degradation model in saturation channel. Accordingly, the fog-free image can be restored with the estimated intensity and saturation components. In the end, the proposed method is tested on several foggy images and assessed by two no-reference indexes. Experimental results reveal that our method is relatively superior to three groups of relevant and state-of-the-art defogging methods.

An Efficient Attentional Image Dehazing Deep Network Using Two Color Space (ADMC2-net)

“…Single image Dehazing has become a topic of interest in recent years, due to its great importance in many computer vision-related fields. The traditional dehazing techniques predominantly relied on image processing, involving the direct manipulation of pixels or the utilization of manually crafted priors [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

“…In the literature, researchers first have used image enhancement based methods to enhance image intrinsic properties (such as contrast, brightness) and details. In [ 1 , 2 , 3 ], authors have employed common contrast enhancement techniques to enhance the visibility of hazy images. However, these techniques can only handle hazy images with thin haze and would lead to some distortions and halo effects in case of thick haze.…”

Study on the enhancement method of online monitoring image of dense fog environment with power lines in smart city

“…It does not take into account the physical process of fog generation. Traditional image contrast enhancement methods include histogram redistribution (Zhou et al, 2016), intensity transformation (Sangeetha and Anusudha, 2017), homomorphic filtering (Seow and Asari, 2006), wavelet transform (Jun and Rong, 2013), and Retinex algorithm (Jobson et al, 1997b). In Retinex theory, the image is made up of the incident element which represents the brightness information around the object and the reflection element which reflects the reflection ability of itself, then the single scale Retinex algorithm (SSR) is proposed.…”

Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

“…As a first entry to the research, considering the haze phenomenon as a contrast or noise reduction problem, the traditional image processing methods are used to lessen haze particles from single hazy images. In [1][2][3], the authors attempted to remove haze particles from single hazy images by using the histogram equalization technique to improve the contrast of those images. Chen et al [4] proposed the homomorphic filtering for image dehazing task.…”