An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering

Liu, Yidan; Xu, Huiping; Shang, Dinghui; Li, Chen; Quan, Xiangqian

doi:10.3390/s19245567

Cited by 20 publications

(5 citation statements)

References 28 publications

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“…One general model of the underwater imaging process is the Jaffe-McGlamery model, where the irradiance of a monochromatic underwater image is formulated as the linear combination of the following three components: the direct component, the absorption component, and the scattering component [18,19]. In our team, to tackle the color deviation caused by artificial lighting, we proposed a new model of underwater image degradation, where the parameters of deep-sea lamps were added [20]. In [21], Guo et al presented a model representing the absorption, scattering, and refraction of water, lenses, and image sensors.…”

Section: Related Work 121 Color Restoration With the Prior Informatio...mentioning

confidence: 99%

The Color Improvement of Underwater Images Based on Light Source and Detector

Quan

Wei

et al. 2022

Sensors

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As one of the most direct approaches to perceive the world, optical images can provide plenty of useful information for underwater applications. However, underwater images often present color deviation due to the light attenuation in the water, which reduces the efficiency and accuracy in underwater applications. To improve the color reproduction of underwater images, we proposed a method with adjusting the spectral component of the light source and the spectral response of the detector. Then, we built the experimental setup to study the color deviation of underwater images with different lamps and different cameras. The experimental results showed that, a) in terms of light source, the color deviation of an underwater image with warm light LED (Light Emitting Diode) (with the value of Δa*2+Δb*2 being 26.58) was the smallest compared with other lamps, b) in terms of detectors, the color deviation of images with the 3×CMOS RGB camera (a novel underwater camera with three CMOS sensors developed for suppressing the color deviation in our team) (with the value of Δa*2+Δb*2 being 25.25) was the smallest compared with other cameras. The experimental result (i.e., the result of color improvement between different lamps or between different cameras) verified our assumption that the underwater image color could be improved by adjusting the spectral component of the light source and the spectral response of the detector. Differing from the color improvement method with image processing, this color-improvement method was based on hardware, which had advantages, including more image information being retained and less-time being consumed.

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Section: Related Work 121 Color Restoration With the Prior Informatio...mentioning

confidence: 99%

The Color Improvement of Underwater Images Based on Light Source and Detector

Quan

Wei

et al. 2022

Sensors

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“…Zhang et al [ 42 ] generate the chromaticity intrinsic image (CII) in a log chromaticity space that is robust to illumination variations by combining Phong’s Model and Lambertian Model. Liu et al [ 43 ]. use fusion-based descattering and color tone correction to enhance the illumination of underwater image.…”

Section: Related Workmentioning

confidence: 99%

A High-Performance Face Illumination Processing Method via Multi-Stage Feature Maps

Ling

Lin

et al. 2020

Sensors

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In recent years, Generative Adversarial Networks (GANs)-based illumination processing of facial images has made favorable achievements. However, some GANs-based illumination-processing methods only pay attention to the image quality and neglect the recognition accuracy, whereas others only crop partial face area and ignore the challenges to synthesize photographic face, background and hair when the original face image is under extreme illumination (Image under extreme illumination (extreme illumination conditions) means that we cannot see the texture and structure information clearly and most pixel values tend to 0 or 255.). Moreover, the recognition accuracy is low when the faces are under extreme illumination conditions. For these reasons, we present an elaborately designed architecture based on convolutional neural network and GANs for processing the illumination of facial image. We use ResBlock at the down-sampling stage in our encoder and adopt skip connections in our generator. This special design together with our loss can enhance the ability to preserve identity and generate high-quality images. Moreover, we use different convolutional layers of a pre-trained feature network to extract varisized feature maps, and then use these feature maps to compute loss, which is named multi-stage feature maps (MSFM) loss. For the sake of fairly evaluating our method against state-of-the-art models, we use four metrics to estimate the performance of illumination-processing algorithms. A variety of experimental data indicate that our method is superior to the previous models under various illumination challenges in illumination processing. We conduct qualitative and quantitative experiments on two datasets, and the experimental data indicate that our scheme obviously surpasses the state-of-the-art algorithms in image quality and identification accuracy.

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“…The long wavelengths such as red can only penetrate to a very shallow depth that is not more than approximately 50 meters. This is the reason why underwater images appear bluish green unlike normal images taken above water [1]. It is known that different salinity levels of water, affects the amount of different wavelengths absorption.…”

Section: Introductionmentioning

confidence: 99%

“…Multiplication of the refined inputs with their corresponding confidence maps produces the final enhanced output as seen in Eq. (1).𝐼 𝑒𝑛 = 𝐼 𝐺𝐶 * 𝐶 𝐺𝐶 + 𝐼 𝑊𝐵 * 𝐶 𝑊𝐵 + 𝐼 𝐻𝐸𝐹 * 𝐶 𝐻𝐸𝐹(1)…”

mentioning

confidence: 99%

An End-to-End CNN Approach for Enhancing Underwater Images Using Spatial and Frequency Domain Techniques

Rejal¹,

Nagaty²,

Pester³

2023

ATAIML

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Underwater image processing area has been a central point of interest to many people in many fields such as control of underwater vehicles, archaeology, marine biology research, etc. Underwater exploration is becoming a big part of our life such as underwater marine and creatures research, pipeline and communication logistics, military use, touristic and entertainment use. Underwater images are subject to poor visibility, distortion, poor quality, etc., due to several reasons such as light propagation. The real problem occurs when these images have to be taken at a depth which is more than 500 feet where artificial light needs to be introduced. This work tackles the underwater environment challenges such as as colour casts, lack of image sharpness, low contrast, low visibility, and blurry appearance in deep ocean images by proposing an end-to-end deep underwater image enhancement network (WGH-net) based on convolutional neural network (CNN) algorithm. Quantitative and qualitative metrics results proved that our method achieved competitive results with the previous work methods as it was experimentally tested on different images from several datasets.

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An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering

Cited by 20 publications

References 28 publications

The Color Improvement of Underwater Images Based on Light Source and Detector

The Color Improvement of Underwater Images Based on Light Source and Detector

A High-Performance Face Illumination Processing Method via Multi-Stage Feature Maps

An End-to-End CNN Approach for Enhancing Underwater Images Using Spatial and Frequency Domain Techniques

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