The Retinex-based image dehazing using a particle swarm optimization method
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The Retinex-based image dehazing using a particle swarm optimization method Li-Ping Yao 1,2 & Zhong-liang Pan 1 Received: 30 May 2020 / Revised: 24 August 2020 / Accepted: 2 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
To the best of our knowledge, currently the physical model based method is still an ill posed problem. Additionally, the image enhancement approaches also suffer from the texture preservation issue. Retinex-based approach is proved its effectiveness in image dehazing while the parameter should be turned properly. Therefore, in this paper, the particle swarm optimization (PSO) algorithm is firstly performed to optimize the parameter and the hazed image is converted into hue, saturation, intensity(HSI) for color compensation, In the other hand, the multi-scale local detail upgrading and the bilateral filtering approaches are designed to overcome the dehazing artefacts and edge preservation, which could further improve the overall visual effect of images. Experimental results on natural and synthetic images by using qualitative analysis and frequently used quantitative evaluation metrics illustrate the approving defogging effect of the proposed method. For instance, in a natural image road, our method achieves the higher e for 0.63, γ for 3.21 and H for 7.81, respectively and lower σ for 0.04. In a synthetic image poster, the higher PSNR for 18.17 and SSIM for 0.78 are also acquired compared to other explored approaches in this paper. Besides, the results performed on other underwater and aerial images in this study further demonstrates its defog effectiveness. Keywords Image enhancement . Retinex . Particle swarm optimization algorithm . Nature and synthetic images . Underwater and aerial images
1 Introduction The accumulation of dust and smoke particles in the environment would result in haze, and thereby the outdoor images would be degraded with the result of poor * Zhong-liang Pan [email protected]
1
School of Physics and Telecommunications Engineering, South China Normal University, Guangzhou 510006 Guangdong, China
2
Guangdong Institute of Medical Instruments, Guangzhou 510000 Guangdong, China
Multimedia Tools and Applications
contrast and color fidelity due to the scattering of particles in the atmosphere like haze particles or fog droplets [37]. Based on the fact that the clear visibility of the input image is assumed in many vision algorithms, from basic image segmentation as well as feature extraction to high-level object recognition and measurement [39]. In that way, the hazed image would limit the precision performance of these algorithms and would disturb many applications, such as visual surveillance, driving assistance, etc. [31, 38] With the advancements in computer vision applications, various available image dehazing approaches have paid more attention and have been utilized extensively [5]. The two main existing image dehazing approaches included both image enhancement and image restoration [41]. The image enhancement
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