Hue preserving‐based approach for underwater colour image enhancement

Hou, Guiting; Pan, Zhenkuan; Huang, Baoxiang; Wang, Guodong; Luan, Xin

doi:10.1049/iet-ipr.2017.0359

Cited by 69 publications

(26 citation statements)

References 14 publications

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“…Based on the set of images in the offline phase, we use the image segmentation algorithm to detect the boundaries of the boards and then remove the backgrounds of the wood images. The obtained images are converted from the RGB color space into the HSV color space, and the number of pixels with each value in the H, S, and V channels is calculated, respectively [24]. We calculate the confidence interval at a 95% confidence level for the pixel values in the H, S, and V channels, respectively.…”

Section: Image Preprocessingmentioning

confidence: 99%

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

et al. 2020

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Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly be used in this scenario. Moreover, wooden boards are long, and their images have a high resolution, which may lead to the growth of computational complexity. To overcome these challenges, in this paper, we propose a new mechanism for color classification of wooden boards based on machine vision. The image of the wooden board is preprocessed to subtract irrelevant colors, and the feature vector is extracted based on 3D color histogram to reduce the computational complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through the K-means algorithm. Then, the clustering result can be used in the online classification to classify the new wood image. Furthermore, to process the abnormal images of wooden boards, we propose an improved algorithm with centroid improvement and image filtering. The experimental results verify the effectiveness of the proposed mechanism.

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Section: Image Preprocessingmentioning

confidence: 99%

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

et al. 2020

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“…The undersea environment is different from that on land, there is no source of light. Underwater imaging systems have to rely on the artificial light to provide illumination, which brings problems due to light absorption, light reflection, bending, light scattering and poor visibility [30]. Therefore, image feature extraction method is a key point for diagnosing faults based on image classification.…”

Section: Problem Description On Blade Attachment Of Mctmentioning

confidence: 99%

A Sparse Autoencoder and Softmax Regression based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine

Zheng¹,

Wang²,

Xin³

et al. 2019

Preprint

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The development and application of marine current energy are attracting more and more attention in the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of marine current generation system. In this paper, underwater image is chosen as the fault diagnosing signal after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with the other techniques, experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

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“…Ao and Ma [10] presented an Adaptive Linear Stretch (ALS) method that can adjust the low distribution lightness values and set an adaptable threshold according to the underwater image histogram. Hou et al [11] presented an underwater image enhanvement method named Wavelet-Domain Filtering (WDF) and Constrained Histogram Stretching (CHS) to be operated on HSI and HSV color models, respectively.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Method to Remove Color Cast and Contrast Enhancement for Underwater Image

Wong

Raveendran

Yoshida

et al. 2019

IEICE Trans. Fundamentals

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Light scattering and absorption of light in water cause underwater images to be poorly contrasted, haze and dominated by a single color cast. A solution to this is to find methods to improve the quality of the image that eventually leads to better visualization. We propose an integrated approach using Adaptive Gray World (AGW) and Differential Gray-Levels Histogram Equalization for Color Images (DHECI) to remove the color cast as well as improve the contrast and colorfulness of the underwater image. The AGW is an adaptive version of the GW method where apart from computing the global mean, the local mean of each channel of an image is taken into consideration and both are weighted before combining them. It is applied to remove the color cast, thereafter the DHECI is used to improve the contrast and colorfulness of the underwater image. The results of the proposed method are compared with seven state-of-the-art methods using qualitative and quantitative measures. The experimental results showed that in most cases the proposed method produced better quantitative scores than the compared methods.

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Hue preserving‐based approach for underwater colour image enhancement

Cited by 69 publications

References 14 publications

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

A Sparse Autoencoder and Softmax Regression based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine

An Integrated Method to Remove Color Cast and Contrast Enhancement for Underwater Image

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