Z
Zhibin Yu
Researcher at Ocean University of China
Publications - 73
Citations - 1020
Zhibin Yu is an academic researcher from Ocean University of China. The author has contributed to research in topics: Underwater & Deep learning. The author has an hindex of 15, co-authored 66 publications receiving 631 citations. Previous affiliations of Zhibin Yu include Kyungpook National University.
Papers
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Multi-scale adversarial network for underwater image restoration
TL;DR: This paper proposes an underwater image restoration method based on transferring an underwater style image into a recovered style using Multi-Scale Cycle Generative Adversarial Network (MCycle GAN) System and includes a Structural Similarity Index Measure loss (SSIM loss), which can provide more flexibility to model the detail structural to improve the image restoration performance.
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Automatic plankton image classification combining multiple view features via multiple kernel learning
TL;DR: This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.
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Underwater Image Enhancement With a Deep Residual Framework
TL;DR: According to the underwater image enhancement experiments and a comparative analysis, the color correction and detail enhancement performance of the proposed methods are superior to that of previous deep learning models and traditional methods.
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Deep learning of support vector machines with class probability output networks
TL;DR: A new deep architecture that uses support vector machines with class probability output networks (CPONs) to provide better generalization power for pattern classification problems is proposed and closely approaches the ideal Bayes classifier as the number of layers increases.
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Improving Transfer Learning and Squeeze- and-Excitation Networks for Small-Scale Fine-Grained Fish Image Classification
TL;DR: The experimental results show that the improved transfer learning and squeeze-and-excitation networks method outperforms popular CNNs with higher fish classification accuracy, which indicates its potential applications in combination with other newly updated CNNs.