Single Image Dehazing via Large Sky Region Segmentation and Multiscale Opening Dark Channel Model

Liu, Yun; Li, Hejian; Wang, Minghui

doi:10.1109/access.2017.2710305

Cited by 54 publications

(28 citation statements)

References 32 publications

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“…However, despite the progress in AI-based methods, DCP-based research has continued [22]. For example [8], [9], [23]- [26] are focused on reducing the over-saturated areas generated when DCP is applied over sky regions. These works improved DCP computation by adding an image segmentation stage or implementing quadtree techniques, but their main drawback is the relatively long processing time.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, research efforts have focused on improving the performance measurement of dehazing methods [1], [27]- [29]. The main difference of the method proposed in this work regarding previous dehazing DCP/Fast Guided Filter (FGF) [5], [7], [30], [31] and sky-detection based methods [8], [9], [23]- [25] is the effective combination of DCP and FGF with local Shannon entropy, resulting in a fast and efficient method with remarkable results on outdoorimage dehazing.…”

Section: Introductionmentioning

confidence: 99%

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Fast Single Image Defogging With Robust Sky Detection

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Single Image Defogging With Robust Sky Detection

et al. 2020

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“…Although the visibility of the sky area can be improved, this approach generally reduces the recovery performance among adjacent boundaries. Liu et al [23] proposed a large sky region detection algorithm based on SVM classification, which uses two different strategies to obtain more accurate atmospheric light according to its detection results. Finally, the multiscale open dark channel model is used to adaptively calculate the dark channel for dehazing.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior

Yang

Tang

2020

Algorithms

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The tolerance mechanism based on dark channel prior (DCP) of a single image dehazing algorithm is less effective when there are large areas of the bright region in the hazy image because it cannot obtain the tolerance adaptively according to the characteristics of the image. It will lead to insufficient improvement of the transmission of image, so it is difficult to eliminate the color distortion and block effects in the restored image completely. Moreover, when a dense haze area or a third-party direct light source (sunlight, headlights and reflected glare) is misjudged as sky area, the use of tolerance will cause an inferior dehazing effect such as details lost. Regarding the issue above, this paper proposes an adaptive tolerance estimation algorithm. The tolerance is obtained according to the statistic characteristics of each image to make the estimation of transmission more accurately. The experimental results show that the proposed algorithm not only maintains high operational efficiency but also effectively compensates for the defects of the dark channel prior to some scenes. The proposed algorithm can effectively solve the problem of color distortion recovered by the DCP method in the bright regions of the image.

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“…Available dehazing techniques are usually used for natural outdoor hazy images and can be roughly classified into two categories, i.e., fusion-based techniques [1]- [4] and physically-based methods [5]- [13]. The former one is based on Laplacian pyramid representation which blends the useful information of prepossessed images to achieve haze-free results.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Haze Removal Using Gamma-Correction-Based Dehazing Model

Ding

Guo

2019

IEEE Access

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Haze is evident in most remote sensing (RS) images, particularly for the RS scenes captured in inclement weather, which severely hinders image interpretation. In this paper, two simple yet effective visibility restoration formulas are proposed for RGB-channel RS (RRS) images and multi-spectral RS (MSRS) images, respectively. More specifically, a robust gamma-correction-based dehazing model (RGDM) is first defined, which can better address the non-uniform illumination problem in hazy images. Then, the scene albedo restoration formula (SARF) used for the RRS images is obtained by imposing the existing prior knowledge on this RGDM, which enables us to simultaneously eliminate the interferences of haze and non-uniform illumination. In subsequence, according to Rayleigh's law, an expanded restoration formula (E-SARF) is further developed for MSRS data. Using the proposed E-SARF, the spatially varying haze in each band can be thoroughly removed without using any extra information. The experiments are conducted on the challenging RRS and MSRS images, including images with non-uniform illumination, non-uniform haze distribution, and heavy haze. The results reveal that the SARF and the E-SARF are superior to most other state-of-the-art techniques in terms of both the recover quality and the implementation efficiency.

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Single Image Dehazing via Large Sky Region Segmentation and Multiscale Opening Dark Channel Model

Cited by 54 publications

References 32 publications

Fast Single Image Defogging With Robust Sky Detection

Fast Single Image Defogging With Robust Sky Detection

Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior

Remote Sensing Image Haze Removal Using Gamma-Correction-Based Dehazing Model

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