Underwater image enhancement using contrast limited adaptive histogram equalization and layered difference representation

Ulutaş, Güzin; Üstübi̇oğlu, Beste

doi:10.1007/s11042-020-10426-2

Cited by 44 publications

(17 citation statements)

References 28 publications

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“…Color channel histogram is the most commonly used method of color feature table. To solve the impact of natural light and artificial light on underwater imaging quality, Guzin et al [9] adopted the underwater image enhancement technology of adaptive histogram equalization and layered difference representation with limited contrast, combined the global and local contrast enhancement technology, and carried out local color correction for underwater images. In the module, the contrastenhanced image is converted to HSV space, and the color values of the S and V components of the image are improved by CLAHE, which solves the problem of relatively low underwater image quality.…”

Section: Period Of Traditional Manual Featuresmentioning

confidence: 99%

A Literature Review of Underwater Image Detection

Wang

Jin

et al. 2022

Frontiers in Artificial Intelligence and Applications

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As one of the world’s few large-scale aquaculture countries, the mechanization, output and efficiency are low in Chinese aquaculture industry.with the development of industrial automation technology and computer image processing technology, aquaculture will be automated, scientific management transformation. As a specific application of computer recognition technology, underwater image recognition technology is important to promote aquaculture industry automation and intelligence. In this paper, first of all introduces the existing research of underwater target image recognition, mainly presents the underwater image recognition technology based on deep learning. And then the current problems of underwater image recognition is summarized. Besides, the future development of underwater image recognition technology and its application in wisdom fishery is also discussed, which provides other scholar with some theoretical help and a comprehensive reference.

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Section: Period Of Traditional Manual Featuresmentioning

confidence: 99%

A Literature Review of Underwater Image Detection

Wang

Jin

et al. 2022

Frontiers in Artificial Intelligence and Applications

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“…Since, both the enhancement and restoration techniques are fused, it resulted the image with good human perception. Ulutas and Ustubioglu (2021) blended the local Contrasted Limited Adaptive Histogram Equalisation (CLAHE) and global LDR enhancement technique to enhance the whole image and the brightness of the local features are also focused. Without the detail of the Image Formation Model (IFM), Gao et al (2021) addressed the contrast and color distortion by the method called Local Contrast Correction (LCC) with multiscale fusion technique for underwater images.…”

Section: Related Workmentioning

confidence: 99%

Visibility improvement of underwater turbid image using hybrid restoration network with weighted filter

Muthuraman

Santhanam

2021

Multidim Syst Sign Process

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Due to the attenuation of light passes through water, the captured underwater images suffer from low-contrast, halo artifacts, etc. To address this issue, the hybrid network with a weighted filter is proposed to improve the visibility of the obscured (turbid) images. In the captured image, the brighter pixels (near-to-source) are called foreground regions and the darker pixels (far-from-source) are called background regions. In order to ensure the adaptability of the proposed algorithm, the considered datasets are collected on different atmospheric light such as pond, lake, and fisheries tank. The foreground area of an image can be enhanced using the thresholding and masking technique. The background hazy region can be recovered by a hybrid Dehazenet called Generative Adversarial Network and Convolutional Neural Network. With this, the transmission map with high accuracy and color deviation can be addressed. Then both the regions are blended and the Amended Unsharp Mask filter is used to toughen the distorted edges. Finally, the blended restored image is weighted with a contrast factor to obtain the visibility improved image. The subjective and objective evaluation is done on considering the standard non-reference metric called Underwater Image Quality Measure comprises measures of color, sharpness, and contrast for a variety of water types with different atmospheric light. It is observed that the proposed technique showed a metric improvement of 57% compared to other existing techniques in an average manner. Overall, it is inferred that the proposed technique produces better results in both subjective and objective evaluation, thus it outperforms other state-of-the-art techniques.

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“…To reduce the hazing effect in underwater images using a prior knowledge of the raw images, several models such as dark channel prior (DCP), 12,13 nonlocal (NL), 14 underwater dark channel prior (UDCP), 15 blurriness prior, 16 and histogram distribution prior (HDP) 17 were investigated. Additionally, two‐step decolorization, 18 automatic white balancing method, 19 contrast‐limited adaptive histogram equalization, 20 and curvature variation regularization 21 methods were developed and tested for dehazing the underwater images. These model‐based approaches have the difficulty of finding a suitable mathematical model and estimation of accurate a prior knowledge of the image acquisition environments; therefore, their efficiency in image restoration was found to be limited.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

Sivamani

Nedumaran

2021

Color Research & Application

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Underwater image processing is a vast area of research to identify blurred objects in the ocean or sea environments. The light absorbed and scattered by the underwater medium create artifacts in the acquired images due to low‐light effects, poor visibility, darkness, illumination variation, and color contrast degradation. In this research work, a novel optical image preprocessing framework was proposed to restore the color contrast of images obtained from various water‐colored environments. Domain transform‐based multiscale image decomposition method was used to extract the base, residual, and detail layers. Then, the smoothed image was reconstructed from the base layer by applying the adaptive iterative backward‐projection algorithm. To obtain dehazed images, a multiscale fusion‐based algorithm was developed, which fused the detail layers and reconstructed the images. The outcome of the proposed framework revealed that the contrast degradation and low‐light‐related artifacts present in the acquired images from various water‐colored environments were found to be considerably reduced. The experimental results of the proposed method exhibit scalable improvements over the other methods in terms of visual perception and estimated performance metrics (underwater image quality measure = 6.1186; peak signal‐to‐noise ratio = 26.68; feature points matching = 96 points; semantic image segmentation accuracy = 0.8465 and processing time of 0.11 seconds on a single image). The novelty of this method relies on the fusion of well‐established techniques, which outperformed the advanced techniques like deep learning method in terms of simplicity, speed, performance, datasets, image resolution, and applications.

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Underwater image enhancement using contrast limited adaptive histogram equalization and layered difference representation

Cited by 44 publications

References 28 publications

A Literature Review of Underwater Image Detection

A Literature Review of Underwater Image Detection

Visibility improvement of underwater turbid image using hybrid restoration network with weighted filter

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

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