Weighted haze removal method with halo prevention

Shiau, Yeu-Horng; Chen, P. Y.; Yang, Hung‐Yu; Chen, C. H.; Wang, S. S.

doi:10.1016/j.jvcir.2013.12.011

Cited by 49 publications

(34 citation statements)

References 14 publications

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“…For optimizing the atmospheric light A, we followed the weighted method proposed by Shiau [18]. According to Shiau, the atmospheric light is related to illumination of the restored images.…”

Section: Proposed Methods For De-hazingmentioning

confidence: 99%

“…In the weighted method, the threshold value T is obtained from the grayscale of the input hazy image which is used to divide the dark channel regions into bright and dark regions. We took the patch size as 3x3 for computing dark channel as per Shiau [18]. The pixel values higher than T will be under bright region and the other pixel are under the dark region.…”

Section: Proposed Methods For De-hazingmentioning

confidence: 99%

“…Using the optimized weight, atmospheric light is estimated. Shiau [18] in his work used a fixed weighted value by considering the sum of the probability within dark region and within bright region.…”

Section: Proposed Methods For De-hazingmentioning

confidence: 99%

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Atmospheric Light Estimation using Particle Swarm Optimization for Dehazing

Padmini¹,

Shankar²

2019

IJACSA

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For the past decade, many researchers have been working towards the improvement in the visibility of single hazy images, using the haze image model. According to the haze image model, the hazy-free image is restored by estimating the atmospheric light and transmission from a hazy image. The objective of this proposed work is to improve the perceptibility by decreasing the density of haze in the hazy images. The research work was carried to estimate the optimal value of atmospheric light by tuning the weights using a bioinspired technique called Particle Swarm Optimization (PSO) based on the objective of minimizing the fog density. We have selected a fitness function or objective function which incorporates all statistical features to differentiate a clear image from the hazy image. The results are validated with the state-of-the-art, by measuring fog density of the restored image using Fog Aware Density Evaluator (FADE). Also, the results are validated by measuring the Peak signal to noise ratio (PSNR) and structural similarity index (SSI) using ground truth images from Foggy Road image database (FRIDA). This research work demonstrates better results qualitatively and quantitatively.

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Section: Proposed Methods For De-hazingmentioning

confidence: 99%

Section: Proposed Methods For De-hazingmentioning

confidence: 99%

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Atmospheric Light Estimation using Particle Swarm Optimization for Dehazing

Padmini¹,

Shankar²

2019

IJACSA

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“…However, the direct attenuation term J(x) t(x) may be near zero when the transmission t(x) is near zero [16]. The immediate retrieved scene radiance J is prone to noise.…”

Section:  Image Reconstructionmentioning

confidence: 99%

Evaluation of Image Dehazing by Pso Based Dark Channel Prior

Poonam¹

2017

ijarcs

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Abstract:The following paper signifies that image defogging make use of many outdoor operating systems. Digital images captured in outdoor environment are easily polluted by haze, which will de-grade the conveyed information. Haze detection as well as elimination is actually a challenging job for enhancing the quality of digital images. In general, these pictures are clicked at a long distance from the visual sensor to given scene. The algorithm used here will modify the dark channel prior using existing techniques which have neglected the use of optimized selection of restoration level, so particle swarm optimization will be utilized to monitor and locate the best optimized value for restoration. Adaptive brightness preserving histogram equalization will also be used to remove the uneven illuminate problem of the Haze removal. Thus the proposed technique has the ability to remove the limitations of existing techniques.

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“…He et al [1] proposed a simple but effective image prior-dark channel prior to remove haze from a single input image, which can achieve great haze removal effect. The method has been widely in image processing field [2,3].…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement System Based on Improved Dark Channel Prior

Liu¹,

Zhu²,

Peng³

2015

Proceedings of the 2015 International Conference on Electromechanical Control Technology and Transportation

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Abstract. In order to reduce the bad influence of hazes on the computer vision system, an image dehazing algorithm based on dark channel prior is proposed. Firstly, we analyze the dark channel prior based image dehazing algorithm. Since the complexity of the algorithm is great and the acquired images have halo artifacts effect, the neighborhood similarity method, which gets the difference of the value of dark channel between every pixel and its nearest eight pixels, is introduced. The pixel of minimal difference is redefined as new dark channel. Besides, the adaptive gray stretch and local contrast enhancement algorithms are proposed to increase brightness and definition of the video. Finally, realization of the improved image enhancement algorithms based on DM6467 is analyzed in detail. The results indicate that the method can effectively improve the image quality. IntroductionComputer vision system has been widely used in all aspects of social production and daily life, such as urban transportation, security facilities, video monitoring, etc. However, in foggy weather, the scene light is absorbed or scattered by the atmosphere with a large number of suspended particles (dust particles and water droplets, etc.).Camera image quality deteriorates with poor visibility and color distortion, which greatly affects the reliability and robustness of computer vision system.Image dehazing is a hotspot in the fields of computer vision and image processing. He et al.[1] proposed a simple but effective image prior-dark channel prior to remove haze from a single input image, which can achieve great haze removal effect. The method has been widely in image processing field [2,3].However, as it is based on the assumption that the transmission is locally constant, the patch size will affect the quality of dehazed images [4]. A large patch size leads to bright atmosphere but serious halo artifacts, while a small one can achieve nice dehazing results with little halo artifacts but dim atmosphere.And the process of optimizing the medium transmission in the improved algorithm [5,6] costs too much time, while the computational complexity is too high to be real-time operating for high resolution image.In this paper, an improved dark channel prior based image dehazing algorithm is designed by introducing neighborhood similarity method to reduce halo artifacts without soft matting. In order to improve image quality after image dehazing, some image enhancement algorithms are introduced, such as linear gray expanding and local contrast enhancement. Then, an image enhancement system is developed by applying the image processing algorithms based on the TI's DaVinci technology. The system meets the real-time and good interaction demands.The rest of the paper is organized as follows. Section 2describes the video image enhancement architecture. In Section 3, the proposed method for image enhancement is detailed. Experimental results on image enhancement performance are illustrated in Section 4 followed by the conclusions in Section 5.

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Weighted haze removal method with halo prevention

Cited by 49 publications

References 14 publications

Atmospheric Light Estimation using Particle Swarm Optimization for Dehazing

Atmospheric Light Estimation using Particle Swarm Optimization for Dehazing

Evaluation of Image Dehazing by Pso Based Dark Channel Prior

Image Enhancement System Based on Improved Dark Channel Prior

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