Most existing image dehazing methods tend to boost local image contrast for regions with heavy haze. Without special treatment, these methods may significantly amplify existing image artifacts such as noise, color aliasing and blocking, which are mostly invisible in the input images but are visually intruding in the results. This is especially the case for low quality cellphone shots or compres...

Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with considerat...

Images/videos of outdoor scenes are usually degraded by the turbid medium in the atmosphere. In this paper, a novel single image-based dehazing framework is proposed to remove haze effects from image/video, where we propose two novel image priors, called the pixel-based dark channel prior and the pixel-based bright channel prior. Based on the two priors with the haze imaging model, we propose t...

Outdoor images often suffer from low contrast and limited visibility due to haze, small particles such as dust, mist, and fumes which deflect light from its original course of propagation. Haze has two effects on the image: it weaken the imgage contrast and also adds an additive component to the image, so-called airlight. Recovering a haze-free image can restore the visibility of the scene and ...

The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coars...

Poor visibility in bad weather, such as haze and fog, is a major problem for many applications of computer vision. Thus, haze removal is highly required for receiving high performance of the vision algorithm. In this paper, we propose a new fast dehazing method for real-time image and video processing. The transmission map estimated by an improved guided filtering scheme is smooth and respect w...

Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed in the literature to solve this problem and they have achieved superior results. However, most of the existing methods assume constant atmospheric light model and tend to follow a twostep procedure involving prior-based methods for...

Outdoor images often suffer from low contrast and limited visibility due to haze, small particles such as dust, mist, and fumes which deflect light from its original course of propagation. Haze has two effects on the image: it weaken the imgage contrast and also adds an additive component to the image, so-called airlight. Recovering a haze-free image can restore the visibility of the scene and ...

Removing haze from a single image is a severely ill-posed problem due to the lack of the scene information. General dehazing algorithms estimate airlight initially using natural image statistics and then propagate the incompletely estimated airlight to build a dense transmission map, yielding a haze-free image. Propagating haze is different from other regularization problems, as haze is strongl...

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 ...