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Deep Learning Based Filtering Algorithm for Noise Removal in Underwater Images
Article

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

1
Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakakah 72388, Saudi Arabia
2
Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72345, Saudi Arabia
3
Department of Computer Science & Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad 501218, India
4
Department of Computer Science & Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India
5
Department of Computer Science, Aalto University, 02150 Espoo, Finland
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Celestine Iwendi and Thippa Reddy Gadekallu
Water 2021, 13(23), 3470; https://doi.org/10.3390/w13233470
Received: 20 October 2021 / Revised: 28 November 2021 / Accepted: 29 November 2021 / Published: 6 December 2021
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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. View Full-Text
Keywords: ambient light; block-greedy; CNN; depth estimator; underwater image dehazing ambient light; block-greedy; CNN; depth estimator; underwater image dehazing
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MDPI and ACS Style

Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470

AMA Style

Alenezi F, Armghan A, Mohanty SN, Jhaveri RH, Tiwari P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water. 2021; 13(23):3470. https://doi.org/10.3390/w13233470

Chicago/Turabian Style

Alenezi, Fayadh, Ammar Armghan, Sachi N. Mohanty, Rutvij H. Jhaveri, and Prayag Tiwari. 2021. "Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light" Water 13, no. 23: 3470. https://doi.org/10.3390/w13233470

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