Geometric-Pixel Guided Single-Pass Convolution Neural Network With Graph Cut for Image Dehazing

Alenezi, Fayadh; Ganesan, Subramaniam

doi:10.1109/access.2021.3059115

Cited by 23 publications

(22 citation statements)

References 46 publications

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“…Our PFBN is compared with several state-of-the-art dehazing methods, including NLD [27], AODNet [2], NLDN [28] and a Geometric-Pixel Guided CNN proposed by [20]. Besides, the proposed self-ensemble methods are implemented to enhance the reconstruction ability.…”

Section: B Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

A Progressive Single-Image Dehazing Network With Feedback Mechanism

Liang

Ren

et al. 2021

IEEE Access

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Section: B Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

A Progressive Single-Image Dehazing Network With Feedback Mechanism

Liang

Ren

et al. 2021

IEEE Access

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“…These are attainable via a novel energy function in the depth estimation network. The energy function is based on a novel global-local Markov chain already discussed in detail in [12]. The resultant energy function is optimized by the graph-cut as discussed in Alenezi and Ganesan [12].…”

Section: Minimum Energymentioning

confidence: 99%

“…The energy function is based on a novel global-local Markov chain already discussed in detail in [12]. The resultant energy function is optimized by the graph-cut as discussed in Alenezi and Ganesan [12]. However, in this model, we use the color channel features as representative of both global and local color moments proposed by [57].…”

Section: Minimum Energymentioning

confidence: 99%

“…However, in this model, we use the color channel features as representative of both global and local color moments proposed by [57]. This sets in opposition the super-pixels in the global and local neighborhoods as presented in [12]. Thus, the ambient light used represents the relationship between global and local pixels and super-pixels.…”

Section: Minimum Energymentioning

confidence: 99%

“…The rise of the digital world has driven up the consumer market for various applications [1][2][3][4][5][6][7][8][9][10][11][12][13]. Recently, haze removal has gained increased attention [14].…”

Section: Introductionmentioning

confidence: 99%

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Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light

Alenezi

Armghan

Mohanty³

et al. 2021

Water

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A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the image color channels. We introduce the block-greedy algorithm in a novel Convolutional Neural Network (CNN) proposed to normalize different color channels’ attenuation ratios and select regions with the lowest variance. We address the discontinuity associated with underwater images by transforming both local and global pixel values. We minimize energy in the proposed CNN via a novel Markov random field to smooth edges and improve the final underwater image features. A comparison of the performance of the proposed technique against existing state-of-the-art algorithms using entropy, Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), Underwater Image Colorfulness Measure (UICM), and Underwater Image Sharpness Measure (UISM) indicate better performance of the proposed approach in terms of average and consistency. As it concerns to averagely, UICM has higher values in the technique than the reference methods, which explainsits higher color balance. The μ values of UCIQE, UISM, and UICM of the proposed method supersede those of the existing techniques. The proposed noted a percent improvement of 0.4%, 4.8%, 9.7%, 5.1% and 7.2% in entropy, UCIQE, UIQM, UICM and UISM respectively compared to the best existing techniques. Consequently, dehazed images have sharp, colorful, and clear features in most images when compared to those resulting from the existing state-of-the-art methods. Stable σ values explain the consistency in visual analysis in terms of sharpness of color and clarity of features in most of the proposed image results when compared with reference methods. Our own assessment shows that only weakness of the proposed technique is that it only applies to underwater images. Future research could seek to establish edge strengthening without color saturation enhancement.

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