Nighttime image Dehazing with modified models of color transfer and guided image filter

Jiang, Bo; Meng, Hongqi; Ma, Xiaolei; Wang, Lin; Zhou, Yan; Xu, Pengfei; Jiang, Siyu; Meng, Xianjia

doi:10.1007/s11042-017-4954-9

Cited by 17 publications

(6 citation statements)

References 18 publications

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“…Based on human observation of these images with their corresponding depth maps, the refined image inherits good quality with a bright corresponding depth map as compared to the hazy image and its depth map. Furthermore, by [3] (c) represents Li et al [20], (d) represents Meng et al [22], (e) represents Zhang et al [21] and (f) presents our proposed scheme.…”

Section: Experimental Verification and Evaluation Resultsmentioning

confidence: 99%

“…However, it is associated with color distortions and presence of haze on the edges of the dehazed image. The output of (d) Meng et al [22] presents astonishing results however the restored image inherits an overestimation quality, which results in color infidelities. The output of (d) presents is quite reasonable however recovered Image appears to have low illumination and unnatural colors.…”

Section: Experimental Verification and Evaluation Resultsmentioning

confidence: 99%

“…Li et al [21] proposed a method of haze removal of night-time images by reducing the halos caused by multiple scattering of light near the light sources. Meng et al [22] proposed a method based on the color transfer theory, where the illumination level of nighttime hazy image can be artificially improved through adaptably choosing the reference image, in divergence to the classical model of color transfer with the approach of overall to overall transfer. The modified model highlights on the diverse features of various areas on the original image.…”

Section: Introductionmentioning

confidence: 99%

“…Besides it performs perfectly even though the nighttime image is restricted by the existence of numerous artificial light sources. Moreover, [22] enhanced dehazing scheme is supported and based on the theory of guided image filtering which is implemented, subsequently the significant parameters of dehazing scheme using the atmospheric degradation model are challenging to achieve in the environments of nighttime imaging. In addition, the significant model parameters of guided image filter are nominated in correspondence to the boundary information of initial image instead of the initial image itself, which makes it more beneficial for dehazing image acquired under night time environments.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Method for Night-Time Single Image Dehazing

Owusu-Agyeman¹,

Xie²,

Okae³

2019

JCC

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Images acquired under deprived weather environment are frequently corrupted due to the presence of haze, mist, fog or other aerosols in a form of noise. Haze elimination is essential in computer vision and computational photography applications. Generally, there is the existence of numerous approaches towards haze removal which are mostly meant for hazy images under daytime environments. Although the potency of these proposed approaches has been comprehensively established on daylight hazy images. However these procedures inherit significant limitations on images influenced by night-time hazy environments. Since night time haze removal dehazing remains an ill-posed problem, we proposed a novel method for night-time single image dehazing which is efficient under night-time environments. The proposed scheme is a dark channel-based local image dehazing procedure that locally estimates the atmospheric intensity for each selected mask on a corrupted image independently and not the entire image. This is done in order to overcome the challenge of night-scenes that are exposed to multiple/artificial lights source and spatially non-uniform environmental illumination. We performed an adaptive filtering on the combined dehazed masks to improve the degraded image. We validated the supremacy of the proposed approach in terms of speed and robustness through computer-based experiments. Conclusively, we displayed comparison results with state-of-the-art and extensively emphasized the comparative advantage of our scheme.

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Section: Experimental Verification and Evaluation Resultsmentioning

confidence: 99%

Section: Experimental Verification and Evaluation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Method for Night-Time Single Image Dehazing

Owusu-Agyeman¹,

Xie²,

Okae³

2019

JCC

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“…The data fidelity term denotes the difference between the two images, and the regularization term keeps the smoothness of the reconstructed image. Because of its edge-preserving ability, the GIF has been widely used in image denoising [2,3,4], detail enhancement [5], HDR compression [6], image defogging [7,8] and contrast enhancement [9]. However, the GIF is liable to generate halo artifacts [10] and amplify noise [11].…”

Section: Introductionmentioning

confidence: 99%

Hessian-based weighted guided image filtering

Xie

Cao

2022

Preprint

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Guided image filtering (GIF) is a popular edge-preserving smoothing technique, and the regularization parameter selection plays an important role in the performance of GIF. In this paper, we propose a new guided image filter based on Hessian matrix which consists of the second-order derivatives of an image. More specifically, a new structural measurement index is introduced by using the eigenvalues of the Hessian matrix first, which can distinguish the texture and flat regions of the image. Then the regularization parameter is adjusted based on this Hessian-based second-order structure measurement index, that is, a large regularization parameter is selected to improve the smoothness of the flat regions, while a small regularization parameter is set for the texture regions to preserve the image structure such as edges and corners. To further improve the quality of the filtered images, we also introduce a weighted averaging technique to the linear filter coefficients based on local variance. Experimental results show that the proposed Hessian-based weighted guided image filtering (HWGIF) method outperforms the state-of-the-art approaches in image processing applications such as edge-preserving denoising, detail enhancement, dehazing and HDR compression.

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