An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

Yuan, Xiaoyan; Ju, Mingye; Gu, Zhenfei; Wang, Shuwang

doi:10.3390/info8020057

Cited by 15 publications

(8 citation statements)

References 35 publications

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“…He et al proposed the famous DCP [22]; however, it cannot work well with "inverted low-light image enhancement" as is mentioned previously. Furthermore, its estimation accuracy is unstable since the transmission estimated via DCP with different local patch sizes varies dramatically [40], and "halo" artifacts may be obvious around the abrupt depth. Zhu et al [23] presented the color attenuation prior and therefore has the advantage of high estimation efficiency.…”

Section: Pure Pixel Ratio Priormentioning

confidence: 99%

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

Chen

Zhang

2018

Mathematical Problems in Engineering

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Images captured in low-light conditions are prone to suffer from low visibility, which may further degrade the performance of most computational photography and computer vision applications. In this paper, we propose a low-light image degradation model derived from the atmospheric scattering model, which is simple but effective and robust. Then, we present a physically valid image prior named pure pixel ratio prior, which is a statistical regularity of extensive nature clear images. Based on the proposed model and the image prior, a corresponding low-light image enhancement method is also presented. In this method, we first segment the input image into scenes according to the brightness similarity and utilize a high-efficiency scene-based transmission estimation strategy rather than the traditional per-pixel fashion. Next, we refine the rough transmission map, by using a total variation smooth operator, and obtain the enhanced image accordingly. Experiments on a number of challenging nature low-light images verify the effectiveness and robustness of the proposed model, and the corresponding method can show its superiority over several state of the arts.

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Section: Pure Pixel Ratio Priormentioning

confidence: 99%

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

Chen

Zhang

2018

Mathematical Problems in Engineering

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“…However, it does not work well for most situations due to the ignored imaging theory. The second one is physical dehazing [ 11 ], which is to impose hand craft prior knowledge on the atmospheric scattering model (ASM) to estimate the imaging parameters. Regrettably, the existing prior knowledge cannot be satisfied to all the scenes, thereby hindering the practicality of this type of algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Review of Remote Sensing Image Dehazing

Liu

Wang

et al. 2021

Sensors

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Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated.

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“…Xu et al [16] benefited from the dark channel prior and combined it with the fast bilateral filter to design their dehazing method. In addition, Song et al [17], Yuan et al [18], and Hsieh et al [19] adopted the original DCP in their proposed dehazing methods. However, despite the effectiveness and simplicity of these follow-up methods, the results still have the problem of over-enhancement due to the atmospheric light inaccuracy estimation, and halo effects on the edges.…”

Section: Introductionmentioning

confidence: 99%

Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

Haouassi

2020

Applied Sciences

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Image dehazing plays a pivotal role in numerous computer vision applications such as object recognition, surveillance systems, and security systems, where it can be considered as an introductory stage. Recently, many proposed learning-based works address this significant task; however, most of them neglect the atmospheric light estimation and fail to produce accurate transmission maps. To address such a problem, in this paper, we propose a two-stage dehazing system. The first stage presents an accurate atmospheric light algorithm labeled “A-Est” that employs hazy image blurriness and quadtree decomposition. Te second stage represents a cascaded multi-scale CNN model called CMT n e t that consists of two subnetworks, one for calculating rough transmission maps (CMCNN t r ) and the other for its refinement (CMCNN t ). Each subnetwork is composed of three-layer D-units (D indicates dense). Experimental analysis and comparisons with state-of-the-art dehazing methods revealed that the proposed system can estimate AL and t efficiently and accurately by achieving high-quality dehazing results and outperforms state-of-the-art comparative methods according to SSIM and MSE values, where our proposed achieves the best scores of both (91% average SSIM and 0.068 average MSE).

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An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

Cited by 15 publications

References 35 publications

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

A Review of Remote Sensing Image Dehazing

Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

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