Joint Haze-relevant Features Selection and Transmission Estimation via Deep Belief Network for Efficient Single Image Dehazing

Ling, Zhigang; Li, Xiuxin; Zou, Wen; Liu, Min

doi:10.1109/icpr.2018.8545318

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The DBN is trained layer by layer in an unsupervised manner, it can effectively solve the problems of traditional methods, including the difficulty of feature learning and extraction, easy to fall into the local minimum during training. Moreover, the DBN has strong nonlinear processing ability and good discriminant ability, so that it has been widely used in the many fields, such as image processing [26], human action identification [27], natural language processing [28]. The DBN model has been preliminarily applied in bearing fault diagnosis [29][30][31] and gear fault diagnosis [29,[32][33][34] since it has been applied to aero-engine structural health identification by scholars [34], however, it has not yet been reported for the identification of fault parameters in double-rotor misalignment.…”

Section: Introductionmentioning

confidence: 99%

Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory

Yang

Fanyu

Miao

et al. 2021

Mechanics & Industry

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Misalignment fault is the main factor that affects the normal running of dual-rotor system. Quantitative identification the misalignment fault is an important way to ensure the safe and stable service of the dual-rotor system, while the identification accuracy of traditional methods is low. Aiming at the above problems, this paper proposed a dual-rotor misalignment fault quantitative identification method based on DBN and D-S evidence theory improved by mutual information measure (MIMD-S). Seven groups experiments were conducted and several vibration signals were collected. By comparing it with the traditional methods D-S, and Pignistic improved D-S (PD-S) evidence theory, the results show that the method proposed in this paper improves the accuracy of the misalignment fault quantitative identification of the dual-rotor, the identification error rate was only 0.36%.

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Section: Introductionmentioning

confidence: 99%

Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory

Yang

Fanyu

Miao

et al. 2021

Mechanics & Industry

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“…Single image fog removal has attracted much research in the past and following two major strategies [34]. First cluster of approaches [28][29][30][31][32][33] use a trainable machine learning technique to model the depth map of foggy scenes. These models recover the foggy image patches by learning the knowledge from similar foggy image samples.…”

mentioning

confidence: 99%

Scene-Specific Dark Channel Prior for Single Image Fog Removal

Sivaanpu

Thanikasalam

2021

Int J on Adv. in ICT for Emerging Countries

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Extensive presence of fog in outdoor images severely alters the scene appearance and hence reduces the visibility. Image processing based defogging algorithms are used to restore the details and colour in a single foggy image. Performances of the previous defogging approaches are considerably low since they fail to consider the image-specific cues. In this paper, a novel and simple defogging approach is proposed based on the estimation of depth map by considering the density of fog in local image regions. The proposed approach uses the scene-specific depth map information to compute the dark channel and transmission. The quality of the recovered image is further improved by a post-processing technique. Experimental evaluation performed on FRIDA and FRIDA2 benchmark datasets demonstrates the proposed defogging framework outperforms state-of-the-art approaches. The results and code of this work are open-sourced for reproducibility (https://github.com/RPRO5/Defogging).

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Single Image Defogging using Depth Estimation and Scene-Specific Dark Channel Prior

Kokul

Anparasy

2020

2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer)

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Joint Haze-relevant Features Selection and Transmission Estimation via Deep Belief Network for Efficient Single Image Dehazing

Cited by 3 publications

References 26 publications

Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory

Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory

Scene-Specific Dark Channel Prior for Single Image Fog Removal

Single Image Defogging using Depth Estimation and Scene-Specific Dark Channel Prior

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