Efficient single image dehazing by modifying the dark channel prior

Sebastián Salazar-Colores,Juan-Manuel Ramos-Arreguín,Jesús-Carlos Pedraza-Ortega,J Rodríguez-Reséndiz

doi:10.1186/s13640-019-0447-2

Sebastián Salazar-Colores, Juan-Manuel Ramos-Arreguín + Show 2 more

Open Access

https://doi.org/10.1186/s13640-019-0447-2

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Abstract

Outdoor images can be degraded due to the particles in the air that absorb and scatter light. The produced degradation generates contrast attenuation, blurring, and distortion in pixels, resulting in low visibility. These limit the efficiency of computer vision systems such as target tracking, surveillance, and pattern recognition. In this paper, we propose a fast and effective method, through modification in the computation of the dark channel which significantly reduces the artifacts generated in the restored images presented when using the ordinary dark channel. According to our experimental results, our method produces better results than some state-of-the-art methods in both efficiency and restoration quality. The processing time in tests shows that the method is adequate for images with high-resolution and real-time video processing.

Highlights

The presence of environmental disturbances such as haze and smog gives outdoor images and videos undesirable characteristics that affect the ability of computer vision systems to detect patterns and perform an efficient feature selection and classification
4 Results and discussion In order to have a reference framework about the performance of the method proposed, a comparison was made against four state-of-the-art methods: the classical dark channel prior (DCP) method with a soft-matting refinement stage [8], the method that use a median filter to refine the transmission[10], a new approach using an additional prior known as linear color attenuation prior [11], and a method that use a deep neural network [12]
Tests were done using 22 images acquired from two datasets used commonly in the literature: [15], and from [13, 14], in which the affectations were simulated with random values of t and A

Summary

Introduction

The presence of environmental disturbances such as haze and smog gives outdoor images and videos undesirable characteristics that affect the ability of computer vision systems to detect patterns and perform an efficient feature selection and classification These characteristics are caused by the decrease in contrast and color modification originated by the presence of suspended particles in the air. To improve the performance of systems such as surveillance [1], traffic [2], self-driving vehicles [3] is essential to develop new and better dehazing methods This problem has been studied extensively in the literature with two main approaches: methods that use multiple images [4] and methods that use just a single image [1].

Background

The modified dark channel

Results and discussion

Method

Conclusion