$$\hbox {S}^2\hbox {RGAN}$$: sonar-image super-resolution based on generative adversarial network

Song, Hongtao; Wang, Minghao; Zhang, Liguo; Li, Yang; Jiang, Zhengyi; Yin, Guisheng

doi:10.1007/s00371-020-01986-3

Cited by 15 publications

(3 citation statements)

References 30 publications

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“…Residual-in-Residual Dense Block-GAN [32] The visual outcome of the method was better and was applicable for the applications that don't require the details concerning the place and obtained better running time.…”

Section: Cascadingmentioning

confidence: 97%

“…Residual-in-Residual Dense Block-based GAN was devised by Song et al [32], in which the optimal network with transfer learning was employed to acquire a high-quality image with better perception and low distortion. The slow convergence rate and the unstable learning capability were enhanced through the inclusion of the dense block with enhanced perception and minimal distortion in the reconstructed image.…”

Section: Related Workmentioning

confidence: 99%

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Image Super-Resolution using Generative Adversarial Networks with EfficientNetV2

AlTakrouri¹,

Noor²,

Ahmad³

et al. 2023

IJACSA

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The image super-resolution is utilized for the image transformation from low resolution to higher resolution to obtain more detailed information to identify the targets. The superresolution has potential applications in various domains, such as medical image processing, crime investigation, remote sensing, and other image-processing application domains. The goal of the super-resolution is to obtain the image with minimal mean square error with improved perceptual quality. Therefore, this study introduces the perceptual loss minimization technique through efficient learning criteria. The proposed image reconstruction technique uses the image super-resolution generative adversarial network (ISRGAN), in which the learning of the discriminator in the ISRGAN is performed using the EfficientNet-v2 to obtain a better image quality. The proposed ISRGAN with the EfficientNet-v2 achieved a minimal loss of 0.02, 0.1, and 0.015 at the generator, discriminator, and self-supervised learning, respectively, with a batch size of 32. The minimal mean square error and mean absolute error are 0.001025 and 0.00225, and the maximal peak signal-to-noise ratio and structural similarity index measure obtained are 45.56985 and 0.9997, respectively.

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Section: Cascadingmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Image Super-Resolution using Generative Adversarial Networks with EfficientNetV2

AlTakrouri¹,

Noor²,

Ahmad³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Since their development in 2014, generative adversarial training algorithms have been widely used in various unimodal applications such as scene generation [17], imageto-image translation [18], and image super-resolution [224,225]. To obtain the latest advances in super-resolution algorithms for a variety of remote sensing applications, we invite the reader to refer to the excellent survey article by Rohith et al [226].…”

Section: Generative Adversarial Network Basedmentioning

confidence: 99%

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

et al. 2021

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The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning. This involves the development of models capable of processing and analyzing the multimodal information uniformly. Unstructured real-world data can inherently take many forms, also known as modalities, often including visual and textual content. Extracting relevant patterns from this kind of data is still a motivating goal for researchers in deep learning. In this paper, we seek to improve the understanding of key concepts and algorithms of deep multimodal learning for the computer vision community by exploring how to generate deep models that consider the integration and combination of heterogeneous visual cues across sensory modalities. In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot learning. We also survey current multimodal applications and present a collection of benchmark datasets for solving problems in various vision domains. Finally, we highlight the limitations and challenges of deep multimodal learning and provide insights and directions for future research.

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Beyond view transformation: feature distribution consistent GANs for cross-view gait recognition

2021

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$$\hbox {S}^2\hbox {RGAN}$$: sonar-image super-resolution based on generative adversarial network

Cited by 15 publications

References 30 publications

Image Super-Resolution using Generative Adversarial Networks with EfficientNetV2

Image Super-Resolution using Generative Adversarial Networks with EfficientNetV2

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Beyond view transformation: feature distribution consistent GANs for cross-view gait recognition

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