Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing

Haoqiang Wu,Quanxing Zha,Yiran Fu,Aidong Chen,Hongyuan Jing,Yi-Zhang Jiang

doi:10.1155/2021/3377905

Abstract

Under foggy and other severe weather conditions, image acquisition equipment is not effective. It often produces an image with low contrast and low scene brightness, which is difficult to use in other image-based applications. The dark channel prior dehazing algorithm will cause the brightness of the image to decrease and sometimes introduce halos in the sky area. To solve this problem, we proposed a region similarity optimisation algorithm based on a dark channel prior. First, a vector comprising RGB layer dark channel value was obtained as the original atmospheric ambient light, and then, the proposed regional similarity linear function was used to adjust the atmospheric ambient light matrix. Next, the transmittance of different colour channels was derived and the multichannel soft matting algorithm was employed to produce more effective transmittance. Finally, the atmospheric ambient light and transmittance were substituted into the atmospheric scattering model to calculate clean images. Experimental results show that the proposed algorithm outperformed the existing mainstream dehazing algorithms in terms of both visual judgement and quality analysis with nonhomogeneous haze datasets. The algorithm not only improves the image details but also improves the brightness and saturation of the dehazing result; therefore, the proposed algorithm is effective in the restoration of the hazy image.

Highlights

Fog is a near-surface atmospheric weather phenomenon caused by the desublimation of suspended water droplets in the air
Larger atmospheric ambient light values will make the image brighter, while too small values will result in a darker image overall and affect the accuracy of the estimated transmittance t(X). is is obvious in the dense haze region. erefore, in this study, we proposed an improvement scheme, in which the obtained Ac(X) values are first arranged in a descending order and the first 1% of them are considered as the ideal values of the atmospheric ambient light to ensure that the atmospheric ambient light is more consistent with the real scene as shown in
To evaluate the feasibility and effectiveness of our algorithm more intuitively, we performed comprehensive experiments on a large dataset containing the public O-Haze image dataset, NTIRE 2021 dataset, and real-world images. e O-Haze dataset contains 45 outdoor scene images with the same visual content recorded under fog-free and foggy conditions, from which we stochastically selected six images as the testing set for neural network algorithms and used the others as the training set. e real-world image dataset comprises approximately 150 images, including a public dataset that is available online and self-collected images. e NTIRE 2021 dataset includes 35 pairs of real and outdoor nonhomogeneous hazy images

Summary

Introduction

Fog is a near-surface atmospheric weather phenomenon caused by the desublimation of suspended water droplets in the air. Physical model-based methods use the traditional atmospheric scattering model [10] as the main research object and estimate atmospheric ambient light and transmittance via a priori hypothesis to make them similar to the real scene, realising image defogging processing [11,12,13,14,15,16,17,18,19,20,21,22,23,24] Kaiming He et al [13] proposed a single-image dehazing algorithm using the dark channel prior theory. Like the relative fine dehazing results as those aforementioned networks have, the deep learning-related algorithm relies considerably on big data; this is difficult to implement under sparse samples Algorithms, such as the dark channel a priori based on the traditional atmospheric scattering model, ignore most of the image detail information when estimating the atmospheric ambient light. We ran experiments on the O-Haze dataset, NTIRE 2021 dataset, and real images and analysed the results from both subjective and objective aspects. e results show that the proposed algorithm effectively improves the brightness and saturation of the image and the haze-free image after dehazing has high colour contrast and good visual effect and improves the problem of blue and dark images after the dark channel a priori algorithm processing

Background

Proposed Algorithm

Experimental Results

Conclusion