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unequaled    音标拼音: [ən'ikw,ʌld]
a. 无敌的;无比的;极好的

无敌的;无比的;极好的

unequaled
adj 1: radically distinctive and without equal; "he is alone in
the field of microbiology"; "this theory is altogether
alone in its penetration of the problem"; "Bach was
unique in his handling of counterpoint"; "craftsmen whose
skill is unequaled"; "unparalleled athletic ability"; "a
breakdown of law unparalleled in our history" [synonym:
{alone(p)}, {unique}, {unequaled}, {unequalled},
{unparalleled}]

Unequaled \Un*e"qualed\, a.
Not equaled; unmatched; unparalleled; unrivaled; exceeding;
surpassing; -- in a good or bad sense; as, unequaled
excellence; unequaled ingratitude or baseness. [Written also
{unequalled}.]
[1913 Webster]


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    Fig 1 shows a graph of the absorption coefficient of water with respect to the wavelength of light As light travels further, red wavelengths (625 nm–700 nm) get absorbed 100 times more than blue wavelengths (380 nm–540 nm) [12] As seen in Fig 2, blue wavelengths scatter most in the visible spectrum (380 nm–750 nm) [14] As a consequence, underwater images are tinted with blue and green
  • Cycle-GAN-based synthetic sonar image generation for improved . . .
    One of the main challenges in underwater automatic target recognition is in the data scarcity of underwater sonar imagery This challenge arises especially in data-driven approaches because of the limited training dataset and unknown environmental conditions before the mission Transfer learning and synthetic data generation have been suggested as effective methods to overcome this challenge
  • Real-time GAN-based image enhancement for robust underwater . . . - Frontiers
    To perform real-time GAN-based I2I translation for underwater image enhancement, we adopt the knowledge distillation Aguinaldo et al (2019)for GAN compression to achieve better performance-speed tradeoff The network parameters and computational costs could be heavily reduced after compression while achieving comparable or even better
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    In recent years, there has been a surge of research focused on underwater image enhancement using Generative Adversarial Networks (GANs), driven by the need to overcome the challenges posed by underwater environments Issues such as light attenuation, scattering, and color distortion severely degrade the quality of underwater images, limiting their use in critical applications Generative
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    The objective of the generator network is to generate realistic images in the translated domain that cannot be distinguished from images in the original domain The objective of discriminator networks is to correctly classify original training data as real and generator-synthesized images as fake An unsupervised image-to-image translation
  • rickyrajani Realtime-Ocean-Rendering-WebGL - GitHub
    In this project, we implemented realistic real-time ocean wave rendering in WebGL 2 0, referencing Realistic Real-time Rendering of Ocean Waves and Simulating Ocean Water (Tessendorf 2001) We implemented realistic waves by generating a heightfield using Fast Fourier Transformations and a realistic lighting model which includes reflection, refraction, and alpha blending
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    While in another approach, Hajar et al (Emami et al , 2020) developed a Spatial Attention GAN (SPA-GAN) for image-to-image translation Their model leverages spatial attention in the discriminator to guide the generator toward discriminative regions between source and target domains, leading to more realistic output images
  • Water Simulation and Rendering from a Still Photograph
    We first segment the water surface, estimate rendering parameters, and compute water reflection textures with a combination of neural networks and traditional optimization techniques Then we propose an image-based screen space local reflection model to render the water surface overlaid on the input image and generate real-time water animation
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    The results of the evaluation of the ShipGAN-generated realistic images show that the fake ships are able to be detected as real ships with high confidence scores (approximately 95%) and the sky, the ocean and the ships can be successfully segmented from each other by applying image segmentation algorithms trained by natural images





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