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Search Results (9)
  • Open Access


    Underwater Waste Recognition and Localization Based on Improved YOLOv5

    Jinxing Niu1,*, Shaokui Gu1, Junmin Du2, Yongxing Hao1

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2015-2031, 2023, DOI:10.32604/cmc.2023.040489

    Abstract With the continuous development of the economy and society, plastic pollution in rivers, lakes, oceans, and other bodies of water is increasingly severe, posing a serious challenge to underwater ecosystems. Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste. However, it often causes significant challenges such as noise interference, low contrast, and blurred textures in underwater optical images. A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed, which combines weighted logarithmic transformations, adaptive gamma correction, improved multi-scale Retinex (MSR) algorithm, and the contrast limited adaptive histogram equalization (CLAHE)… More >

  • Open Access


    Underwater Image Enhancement Using Customized CLAHE and Adaptive Color Correction

    Mousa Alhajlah*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5157-5172, 2023, DOI:10.32604/cmc.2023.033339

    Abstract Underwater images degraded due to low contrast and visibility issues. Therefore, it is important to enhance the images and videos taken in the underwater environment before processing. Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images. The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available, low resolution, and blurriness in underwater images caused by the normal camera. Various researchers have proposed different solutions to overcome these problems. Dark channel prior (DCP) is one of the… More >

  • Open Access


    ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression

    M. Jamuna Rani1,*, C. Vasanthanayaki2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1953-1970, 2023, DOI:10.32604/csse.2023.028713

    Abstract Underwater imagery and transmission possess numerous challenges like lower signal bandwidth, slower data transmission bit rates, Noise, underwater blue/green light haze etc. These factors distort the estimation of Region of Interest and are prime hurdles in deploying efficient compression techniques. Due to the presence of blue/green light in underwater imagery, shape adaptive or block-wise compression techniques faces failures as it becomes very difficult to estimate the compression levels/coefficients for a particular region. This method is proposed to efficiently deploy an Extreme Learning Machine (ELM) model-based shape adaptive Discrete Cosine Transformation (DCT) for underwater images. Underwater color image restoration techniques based… More >

  • Open Access


    Visual Enhancement of Underwater Images Using Transmission Estimation and Multi-Scale Fusion

    R. Vijay Anandh1,*, S. Rukmani Devi2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1897-1910, 2023, DOI:10.32604/csse.2023.027187

    Abstract The demand for the exploration of ocean resources is increasing exponentially. Underwater image data plays a significant role in many research areas. Despite this, the visual quality of underwater images is degraded because of two main factors namely, backscattering and attenuation. Therefore, visual enhancement has become an essential process to recover the required data from the images. Many algorithms had been proposed in a decade for improving the quality of images. This paper aims to propose a single image enhancement technique without the use of any external datasets. For that, the degraded images are subjected to two main processes namely,… More >

  • Open Access


    Deep Neural Network Driven Automated Underwater Object Detection

    Ajisha Mathias1, Samiappan Dhanalakshmi1,*, R. Kumar1, R. Narayanamoorthi2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5251-5267, 2022, DOI:10.32604/cmc.2022.021168

    Abstract Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an… More >

  • Open Access


    Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

    Atif Naseer1,*, Enrique Nava Baro1, Sultan Daud Khan2, Yolanda Vila3, Jennifer Doyle4

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5321-5344, 2022, DOI:10.32604/cmc.2022.020886

    Abstract The Norway lobster, Nephrops norvegicus, is one of the main commercial crustacean fisheries in Europe. The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges. The Spanish Oceanographic Institute (IEO) and Marine Institute Ireland (MI-Ireland) conducts annual underwater television surveys (UWTV) to estimate the total abundance of Nephrops within the specified area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the marine… More >

  • Open Access


    A Novel AlphaSRGAN for Underwater Image Super Resolution

    Aswathy K. Cherian*, E. Poovammal

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1537-1552, 2021, DOI:10.32604/cmc.2021.018213

    Abstract Obtaining clear images of underwater scenes with descriptive details is an arduous task. Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors. Consequently, a need for a system that produces clear images for underwater image study has been necessitated. To overcome problems in resolution and to make better use of the Super-Resolution (SR) method, this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network (AlphaGAN) model, named Alpha Super Resolution Generative Adversarial Network (AlphaSRGAN). The model put forth in this paper helps in enhancing the… More >

  • Open Access


    Image Processing of Manganese Nodules Based on Background Gray Value Calculation

    Hade Mao1, 2, Yuliang Liu1, 2, *, Hongzhe Yan1, 2, Cheng Qian3, Jing Xue4

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 511-527, 2020, DOI:10.32604/cmc.2020.09841

    Abstract To troubleshoot two problems arising from the segmentation of manganese nodule images-uneven illumination and morphological defects caused by white sand coverage, we propose, with reference to features of manganese nodules, a method called “background gray value calculation”. As the result of the image procession with the aid this method, the two problems above are solved eventually, together with acquisition of a segmentable image of manganese nodules. As a result, its comparison with other segmentation methods justifies its feasibility and stability. Judging from simulation results, it is indicated that this method is applicable to repair the target shape in the image,… More >

  • Open Access


    Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

    Tingting Yang1, Shuwen Jia1, Hao Ma2, *

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1249-1258, 2020, DOI:10.32604/cmc.2020.05777

    Abstract Underwater imaging is widely used in ocean, river and lake exploration, but it is affected by properties of water and the optics. In order to solve the lower-resolution underwater image formed by the influence of water and light, the image super-resolution reconstruction technique is applied to the underwater image processing. This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology. We research the degradation model of underwater images, and analyze the lower-resolution factors of underwater images in different situations, and compare different traditional super-resolution image reconstruction algorithms. We further show that the algorithm of… More >

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