• Ocean Systems Engineering
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• v.11 no.4
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• pp.331-351
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• 2021

Underwater images are very much different from images taken on land, due to the presence of a higher disturbance ratio caused by the presence of water medium between the camera and the target object. These distortions and noises result in unclear details and reduced quality of the output image. An underwater image restoration method is proposed in this paper, which uses blurriness information, background light neutralization information, and red-light intensity to estimate depth. The transmission map is then estimated using the derived depth map, by considering separate attenuation coefficients for direct and backscattered signals. The estimated transmission map and estimated background light are then used to recover the scene radiance. Qualitative and quantitative analysis have been used to compare the performance of the proposed method against other state-of-the-art restoration methods. It has been shown that the proposed method can yield good quality restored underwater images. The proposed method has also been evaluated using different qualitative metrics, and results have shown that method is highly capable of restoring underwater images with different conditions. The results are significant and show the applicability of the proposed method for underwater image restoration work.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.23-27
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• 2024

Underwater images are typically degraded due to color distortion, light absorption, scattering, and noise from artificial light sources. Restoration of these images is an essential task in many underwater applications. In this paper, we propose a two-phase deep learning-based method, Underwater Deep Curve Estimation (UWDCE), designed to effectively enhance the quality of underwater images. The first phase involves a white balancing and color correction technique to compensate for color imbalances. The second phase introduces a novel deep learning model, UWDCE, to learn the mapping between the color-corrected image and its best-fitting curve parameter maps. The model operates iteratively, applying light-enhancement curves to achieve better contrast and maintain pixel values within a normalized range. The results demonstrate the effectiveness of our method, producing higher-quality images compared to state-of-the-art methods.

• Proceedings of the KIEE Conference
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• 1998.07d
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• pp.1537-1539
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• 1998

In this study, 3-D underwater object restoration using ultrasonic transducer fabricated with PZT-Polymer 1-3 type composite are presented. Using the acquired underwater object data 16${\times}$16 pixels. Modified SCL neural networks using the 16${\times}$16 low resolution image was used for underwater object restoration of 32${\times}$32 high resolution Image.

• Proceedings of the KIEE Conference
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• 1997.07d
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• pp.1373-1375
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• 1997

In this study, 3-D underwater object restoration using ultrasonic sensor fabricated with porous PZT-Polymer 3-3 type composite are presented Using the acquired underwater object data $16{\times}16$ pixels, Modified SCL neural networks using the $16{\times}16$ low resolution image was used for underwater object restoration of $32{\times}32$ high resolution image.

• Electrical & Electronic Materials
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• v.9 no.8
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• pp.825-830
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• 1996

In this study, Characteristics of Ultrasonic Transducer fabricated with porous piezoelectric resonator, 3-D underwater object restoration using the self made ultrasonic transducer and modified SCL(Simple Competitive Learning) neural network are investigated. The self-made transducer was satisfied the required condition of ultrasonic transducer in water, and the modified SCL neural network using the acquired object data 16*16 low resolution image was used for object restoration of $32{\times}32$ high resolution image. The experimental results have shown that the ultrasonic transducer fabricated with porous piezoelectric resonator could be applied for SONAR system.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.48 no.6
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• pp.456-461
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• 1999

In this study, the characteristics of Ultrasonic Transducer fabricated with PZT-Polymer 1-3 type piezoelectric ceramic/polymer composite are investigated. 3-D underwater object restoration using the self-made ultrasonic transducer and modified SCL(Simple Competitive Learning) neural network was presented. The ultrasonic transducer was satisfied with the required condition of commerical ultrasonic transducer in underwater. The modified SCL neural network using the acquired object data $16\times16$ low resolution image was used for object restoration of $32\times32$ high resolution image. The experimental results have shown that the ultrasonic transducer fabricated with PZT-Polymer 1-3 type piezoelectric ceramic/polymer composite could be applied for SONAR system.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.12-20
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• 2018

Efforts have been made to reconstruct low-resolution underwater images to high-resolution ones by using the image SR (Super-Resolution) method, all to improve efficiency when acquiring side-scan sonar images. As side-scan sonar images are similar with the optical images with respect to exploiting 2-dimensional signals, conventional image restoration methods for optical images can be considered as a solution. One of the most typical super-resolution methods for optical image is a sparse coding and there are studies for verifying applicability of sparse coding method for underwater images by analyzing sparsity of underwater images. Sparse coding is a method that obtains recovered signal from input signal by linear combination of dictionary and sparse coefficients. However, it requires huge computational load to accurately estimate sparse coefficients. In this study, a sparse coding based underwater image super-resolution method is applied while a selective reconstruction method for object region is suggested to reduce the processing time. For this method, this paper proposes an edge detection and object and non object region classification method for underwater images and combine it with sparse coding based image super-resolution method. Effectiveness of the proposed method is verified by reducing the processing time for image reconstruction over 32 % while preserving same level of PSNR (Peak Signal-to-Noise Ratio) compared with conventional method.