Abstract

Visibility restoration of an image degraded by scattering media (such as haze, fog, smog) is a challenging task which requires careful design of computational methods. The fundamental problem which limits clear image reconstruction is the random diffusion of light in the scattering medium. Recently, the dark channel prior method has emerged as a powerful approach for haze removal under adverse weather conditions. However, this method requires high computational time, and fails to achieve haze-free image in the sky region. To overcome these problems, we propose a novel imaging method based on quadtree theory combined with transmission optimization (QTCTO) to restore the visibility of images degraded by scattering medium. The proposed method uses the quadtree theory (for region segmentation) to estimate the global atmospheric light, while the transmission in the media is estimated by coefficient modification for the dark channel. The experimental results show that the proposed method is robust against scattering (under smoke and turbid water conditions) and has the advantage of low computational time. The proposed method can be used for security surveillance, remote sensing, and various military applications.

Highlights

  • IntroductionImage restoration under scattering (turbid) media has been a topic of constant research recently

  • Image restoration under scattering media has been a topic of constant research recently

  • We propose a novel imaging method based on quadtree theory combined with transmission optimization (QTCTO) to restore the visibility of images degraded by scattering medium

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Summary

Introduction

Image restoration under scattering (turbid) media has been a topic of constant research recently. Studies on image visibility restoration have been conducted in the past from two aspects. One of these methods is the contrast enhancement method based on image processing [1]–[5], and the other is the image restoration method based on physical model [6]–[9]. The contrast enhancement method applies image processing algorithm to rectify the degraded image without taking into account the physical phenomena and reasons behind degradation. This method loses more information and fails to recover images efficiently.

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