Haze removal is a difficult problem due the inherent ambiguity between the haze and the underlying scene. All images contain some noise due to sensor error that can be amplified in the haze removal process. A number of methods have been proposed for haze removal. In this paper proposes a simple but effective image prior to remove haze from a single input image. By using single image it removes ...

Multispectral satellite images are often affected by haze and/or cirrus. A previous paper conferred a haze removal method that calculates a haze thickness based on a local search of dark objects. The haze-free signal is restored by subtracting the HTM from the affected image. The HTM method is substantially improved by employing the 1.38-μm cirrus band. The top-of-troposphere reflectance cirrus...

In this paper, we implement a dehazing algorithm using dark pixel detection in MatLab and C++/OpenCV. The algorithm is originally proposed in Yu et al.’s paper. The simplicity and effectiveness of the algorithm make it possible for us to run the C++/OpenCV implementation on Android phones through android NDK with reasonable speed. Keywords—dehaze; image processing; mobile application; computer ...

Image dehazing is one of the most important research area in image processing and pattern analysis. Haze is naturally an atmospheric effect. And it is the combination of air light and attenuation process. Air light increases the whiteness of the image and attenuation effect reduces the contrast. Haze removal algorithms are important in many vision applications. This paper reviewed various haze ...

Images of outdoor scenes are usually degraded under bad weather conditions and bad environment which results in a hazy image. The most of haze removal methods based on a single image have ignored the effects of sensor blur and noise. Therefore, in this paper we propose a simple but effective image priodark channel prior to remove haze from a single input image. We employ an extremum approximate...

Abstrtract : Haze formation is the combination of airlight and attenuation. Attenuation decreases the contrast and airlight increases the whiteness in the scene. Atmospheric conditions created by floting particles such as fog and haze, severely degrade image quality. Removing haze from a single image of a weather-degraded scene found to be a difficult task because the haze is dependent on the u...

We introduce an improved single image haze removal algorithm, which combines dark channel prior (DCP) and histogram specification. First, the dark channel prior knowledge proposed by Kaiming He is analyzed and a conclusion is drawn that the haze removal image based on dark channel prior will have a tendency to dim and indistinct in some specific situations. Especially, when cleaning the haze in...

Images of outdoor scenes often contain degradation due to haze, resulting in contrast reduction and color fading. For many reasons one may need to remove these effects. Unfortunately, haze removal is a difficult problem due the inherent ambiguity between the haze and the underlying scene. Furthermore, all images contain some noise due to sensor (measurement) error that can be amplified in the h...

This paper presents a review on the different haze removal techniques. Haze brings trouble to many computer vision/graphics applications as it diminishes the visibility of the scene. Haze is formed due to the two fundamental phenomena that are attenuation and the air light. Attenuation reduces the contrast and air light increases the whiteness in the scene. Haze removal techniques recover the c...

In this paper, we propose a simple but powerful prior, color attenuation prior, for haze removal from a single input hazy image. By creating a linear model for modelling the scene depth of the hazy image under this novel prior and learning the parameters of the model by using a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can ...