Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

Fu, Hui; Wu, Bin; Shao, Yanhua; Zhang, Hongying

doi:10.1109/access.2018.2886563

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…In table 5, the time consumption [39], [41] of five approaches have also been compared. It can be seen that the time complexity of our strategy is equivalent to the algorithm [10].…”

Section: The Efficiencymentioning

confidence: 99%

Multi-Feature-Based Bilinear CNN for Single Image Dehazing

Shao

2019

IEEE Access

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Image dehazing has been a great challenge in the process of adjusting haze images. In this paper, an effective and accurate dehazing method based on atmospheric scattering model is proposed. Since the dark channel is not applicable to sky areas, single-threshold segmentation combined with quad-tree partition technique is adopted to position and estimate the ambient light A* rapidly and accurately. In order to optimize transmittance, we employ a new convolutional network architecture, multi-feature-based bilinear CNN, which can mitigate the halo effect around the abrupt edges and restrain image noise, and the whole transmittance estimation process can be divided into three main parts: feature extraction, nonlinear mapping, and image reconstruction. A large number of outdoor haze image test results show that our optimized method has better experimental results than the existing methods. INDEX TERMS Single-threshold segmentation, quad-tree, multi-feature, bilinear, dehazing.

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“…In table 5, the time consumption [39], [41] of five approaches have also been compared. It can be seen that the time complexity of our strategy is equivalent to the algorithm [10].…”

Section: The Efficiencymentioning

confidence: 99%

Multi-Feature-Based Bilinear CNN for Single Image Dehazing

Shao

2019

IEEE Access

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“…Fu et al introduced a dehazing algorithm based on hue difference in foggy images, which focuses on hue‐difference features of original and semi‐inverse haze images

H_{s i}^{c} ((), x, y) = \max ([], H^{c} ((), x, y), 1 - H^{c} ((), x, y)), c \in ({}, R, G, B) .

In Equation ,

H_{s i}^{c}

( x , y ) and H c ( x , y ) represent a semi‐inverse haze images and an RGB color channels of haze images, respectively. The smaller pixel values of H c ( x , y ), which dynamically range in [0,0.5], can be replaced by bigger semi‐inverse values in [0.5,1] by Equation .…”

Section: Our Approachmentioning

confidence: 99%

“…Fu et al 21 introduced a dehazing algorithm based on hue difference in foggy images, which focuses on hue-difference features of original and semi-inverse haze images…”

Section: Hue-differencementioning

confidence: 99%

MFDC‐net: Multifeature‐based deep convolutional neural network for single image haze removal

Jing-juan

2019

Concurrency and Computation

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Summary When it comes to the dehazing method based on the ambient light scattering model, there are ill‐conditioning problems of great concern. Therefore, the inaccurate estimation of the global atmospheric light and transmittance can bring about hue error phenomena, halo artifacts, and image noises. In this paper, we present an effective and efficient approach to solve above problems. Firstly, we adopt single‐threshold segmentation method and quad‐tree algorithms, which can break through the limitation that the dark channel prior theory (DCP) is not suitable for sky region, to quickly and accurately locate and estimate the global atmospheric light. Secondly, we design a new deep convolutional network architecture, which can effectively suppress the halo effect of image edges and restrain image noises, to optimize transmittance. Numerous of outdoor and synthesis haze image test results indicate that the optimized approach could shorten the processing time greatly. Besides, with more accurate global atmospheric light value and transmittance, the image noise and the halo artifact can be effectively suppressed and removed respectively, the hue error phenomenon improved, and the image contrast significantly enhanced in the dehazing results.

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“…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%