Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Xiuyuan Wang,Huaibo Song,Chenghai Yang,Jian Zhang

doi:10.25165/j.ijabe.20181102.3357

Abstract

Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. In order to address the above-mentioned problems caused by traditional image dehazing methods, an improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the PSNR and entropy with the proposed method increased by 21.81% and 5.71%, and MSE decreased by 40.07% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95% and entropy by 2.04% and a decrease of MSE by 84.78%. The results from this study can provide a reference for agricultural field monitoring. Keywords: agricultural monitoring, image dehazing, monitoring image, dark channel prior (DCP), brightness promoting DOI: 10.25165/j.ijabe.20181102.3357 Citation: Wang X Y, Yang C H, Zhang J, Song H B. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. Int J Agric & Biol Eng, 2018; 11(2): 170–176.

Highlights

Accurate agricultural monitoring systems are of great importance for conserving resources, reducing pollution, improving the environment and efficiency[1]
With the rapid development of machine vision technology, high-definition images have been increasingly used for accurate monitoring of crop growth status and field conditions
The degraded images may become grayish white with low contrast[2]. They contain less information than the images captured in ideal conditions, making difficulties for feature extraction and other usage

Summary

Introduction

Accurate agricultural monitoring systems are of great importance for conserving resources, reducing pollution, improving the environment and efficiency[1]. With the rapid development of machine vision technology, high-definition images have been increasingly used for accurate monitoring of crop growth status and field conditions. Under the influence of fog, haze and other adverse weather conditions, images are usually degraded. The degraded images may become grayish white with low contrast[2]. They contain less information than the images captured in ideal conditions, making difficulties for feature extraction and other usage. It is important to dehaze images taken under the conditions of fog and haze for environmental improvement, disaster prevention and mitigation, and yield monitoring[3]. Image dehazing has a wide range of practical applications and has received increasing attention[4,5]

Objectives

Methods

Results

Conclusion