Image Haze Removal Based on Superpixels and Markov Random Field

Tan, Yibo; Guo-yu, Wang

doi:10.1109/access.2020.2982910

Cited by 8 publications

(5 citation statements)

References 50 publications

(35 reference statements)

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“…Jiang et al [9] proposed a lightly recurrent network for video dehazing, which makes it possible to train such a large dataset in a limited amount of time. Tan et al [10] introduced a novel method in which a random Markov field was employed to obtain transmission map T .…”

Section: Related Work a Data-driven Single-image Dehazing Methodsmentioning

confidence: 99%

A Progressive Single-Image Dehazing Network With Feedback Mechanism

Liang

Ren

et al. 2021

IEEE Access

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Section: Related Work a Data-driven Single-image Dehazing Methodsmentioning

confidence: 99%

A Progressive Single-Image Dehazing Network With Feedback Mechanism

Liang

Ren

et al. 2021

IEEE Access

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“…Achanta et al [27] calculated superpixels by simple linear iterative clustering (SLIC), which uses a 5-D space with the L * , a * and b * values of the CIELAB color space and the x, y pixel coordinates for local clustering. Several approaches have been proposed using image segmentation with SLIC [9], [25], [28], [29]. The superpixel is used for segmentation of the sky region using object [25] or initial transmission estimation [25], [28], [29].…”

Section: B Superpixel Algorithmmentioning

confidence: 99%

“…Nishino et al [8] introduced a Bayesian probability algorithm that jointly estimates the depth and scene albedo from a single image. Additionally, several image enhancement methods such as histogram equalization, wavelet transform, and Retinex methods exist [9].…”

Section: Introductionmentioning

confidence: 99%

Single Image Dehazing of Road Scenes Using Spatially Adaptive Atmospheric Point Spread Function

et al. 2021

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Image haze removal is essential in autonomous driving as the outdoor images captured during unfavorable weather conditions, such as haze or snow, are affected by poor visibility. Much research has been done to overcome image degradation such as low contrast and faded color due to haze. However, in the traditional model, a phenomenon is neglected that several particles simultaneously involved in light acquisition. To address this problem, we propose a novel single image dehazing method based on the spatially adaptive atmospheric point spread function (APSF). We developed a module that estimates the APSF to overcome the limitations of the spatially invariant APSF which used in existing dehazing algorithms. The key factor in the estimation is that road scenes with haze have different statistical characteristic from common hazy images in color and resolution. Furthermore, the APSF on the traffic signs or lights is estimated by generating superpixels to prevent halo artifacts around the sharp edges of the images. We adopted the total variation model as a regularization functional to reduce halo and unnatural artifacts that may occur during deconvolution. The haze-free images from the proposed method tested whether the proposed method can enhance the performance of vision algorithms for autonomous driving. The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods enhancing the performance of the vision algorithms. Moreover, additional experiments demonstrated the effectiveness of the proposed method for quantitative and qualitative comparison with the state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 99%

“…Several approaches have been proposed that consist of using superpixels for haze removal [58][59][60][61]. In the superpixel domain, Tan and Wang [59] obtained a transmission map and then improved the transmission map by using a Markov random field. Wang et al [60] used the superpixel to estimate the transmission of sky and non-sky area in order to reduce halo artifacts around sharp edges and color distortion in sky area.…”

Section: Introductionmentioning

confidence: 99%

Region Adaptive Single Image Dehazing

Kim

2021

Entropy

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Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate transmissions of bright areas or objects, which may cause color distortions during dehazing. This paper proposes a new single-image dehazing method that combines dark channel prior with bright channel prior in order to overcome the limitations of dark channel prior. A patch-based robust atmospheric light estimation was introduced in order to divide image into regions to which the DCP assumption and the BCP assumption are applied. Moreover, region adaptive haze control parameters are introduced in order to suppress the distortions in a flat and bright region and to increase the visibilities in a texture region. The flat and texture regions are expressed as probabilities by using local image entropy. The performance of the proposed method is evaluated by using synthetic and real data sets. Experimental results show that the proposed method outperforms the state-of-the-art image dehazing method both visually and numerically.

show abstract

Image Haze Removal Based on Superpixels and Markov Random Field

Cited by 8 publications

References 50 publications

A Progressive Single-Image Dehazing Network With Feedback Mechanism

A Progressive Single-Image Dehazing Network With Feedback Mechanism

Single Image Dehazing of Road Scenes Using Spatially Adaptive Atmospheric Point Spread Function

Region Adaptive Single Image Dehazing

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