Structural-Correlated Self-Examples Based Superresolution of Single Remote Sensing Image

Shen, Huifang; Hou, Biao; Wen, Zhen; Jiao, Licheng

doi:10.1109/jstars.2018.2847450

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…SR approaches used to be based on interpolation [ 5 ] and degradation [ 6 ] models. Currently, learning-based method [ 1 , 7 ] have received wide attention, among which deep learning models have shown powerful performance in image SR [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Fast Image Super-Resolution Using Particle Swarm Optimization-Based Convolutional Neural Networks

Zhou

Xiong

2023

Sensors

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Image super-resolution based on convolutional neural networks (CNN) is a hot topic in image processing. However, image super-resolution faces significant challenges in practical applications. Improving its performance on lightweight architectures is important for real-time super-resolution. In this paper, a joint algorithm consisting of modified particle swarm optimization (SMCPSO) and fast super-resolution convolutional neural networks (FSRCNN) is proposed. In addition, a mutation mechanism for particle swarm optimization (PSO) was obtained. Specifically, the SMCPSO algorithm was introduced to optimize the weights and bias of the CNNs, and the aggregation degree of the particles was adjusted adaptively by a mutation mechanism to ensure the global searching ability of the particles and the diversity of the population. The results showed that SMCPSO-FSRCNN achieved the most significant improvement, being about 4.84% better than the FSRCNN model, using the BSD100 data set at a scale factor of 2. In addition, a chest X-ray super-resolution images classification test experiment was conducted, and the experimental results demonstrated that the reconstruction ability of this model could improve the classification accuracy by 13.46%; in particular, the precision and recall rate of COVID-19 were improved by 45.3% and 6.92%, respectively.

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Section: Introductionmentioning

confidence: 99%

Fast Image Super-Resolution Using Particle Swarm Optimization-Based Convolutional Neural Networks

Zhou

Xiong

2023

Sensors

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“…Moreover, the motion blur appeared on the image when the poor handling of the acquisition device between the scenes. Besides that, the acquisition system may introduce a noisy image due to limitations of the sensor's device and additional signals during the transmission medium [18]- [33]. However, the hardware improvement not always reliable in most cases.…”

Section: Introductionmentioning

confidence: 99%

Improving the iterative back projection estimation through Lorentzian sharp infinite symmetrical filter

Rahim

Yaakob

Lee

et al. 2022

IJECE

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<span>This study proposed an enhancement technique for improvising the estimation technique in iterative back projection (IBP) by using the Lorentzian error function with a sharp infinite symmetrical filter (SISEF). The IBP estimation is an iteratively based error correction that can minimize the error reconstruction significantly. However, the IBP has a drawback in that it suffers from jaggy and ringing artifacts as a result of the iterative reconstruction method and the absence of edge guidance. Furthermore, because the IBP estimator tended to oscillate at the same solution frequently, numerous iterations were required. Therefore, this study proposed edge enhancement to enhance the estimator by using the combination of the IBP with Lorentzian SISEF to produce a finer high-resolution output image. As a result, the SISEF is used to improvise the estimator by providing high accuracy of edge detail information for enhancing the edge image. At the same time, the Lorentzian error norm helps to increase the robustness of the IBP algorithm from contamination of additional noise and the ringing artifacts.</span>

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An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network

et al. 2020

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Image super-resolution (SR) technique can improve the spatial resolution of images without upgrading the imaging system. As a result, SR promotes the development of high resolution (HR) remote sensing image applications. Many remote sensing image SR algorithms based on deep learning have been proposed recently, which can effectively improve the spatial resolution under the constraints of HR images. However, images acquired by remote sensing imaging devices typically have lower resolution. Hence, an insufficient number of HR remote sensing images are available for training deep neural networks. In view of this problem, we propose an unsupervised SR method that does not require HR remote sensing images. The proposed method introduces a generative adversarial network (GAN) that obtains SR images through the generator; then, the SR images are downsampled to train the discriminator with low resolution (LR) images. Our method outperformed several methods in terms of the quality of the obtained SR images as measured by 6 evaluation metrics, which proves the satisfactory performance of the proposed unsupervised method for improving the spatial resolution of remote sensing images.

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Structural-Correlated Self-Examples Based Superresolution of Single Remote Sensing Image

Cited by 4 publications

References 32 publications

Fast Image Super-Resolution Using Particle Swarm Optimization-Based Convolutional Neural Networks

Fast Image Super-Resolution Using Particle Swarm Optimization-Based Convolutional Neural Networks

Improving the iterative back projection estimation through Lorentzian sharp infinite symmetrical filter

An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network

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