Super Resolution Imaging Applied to Scientific Images

Chaudhuri, Subhasis

doi:10.21236/ada473125

Cited by 9 publications

(11 citation statements)

References 27 publications

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“…High-frequency information is important for constructing important edges in the high-resolution approximation. Evidently, this leads to the development of Super Resolution (SR) algorithms [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] which synthesize missing high-resolution pixel information that is not explicitly found in the LR measurements. SISR methods have been widely advanced by the breakthroughs in deep learning.…”

Section: Introductionmentioning

confidence: 99%

Multi-step reinforcement learning for medical image super-resolution

Bouffard

Pop

Ebrahimi

2023

Medical Imaging 2023: Image Processing

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We apply a multi-agent Reinforcement Learning (RL) algorithm to single image super-resolution (SISR). In the area of RL, intelligent agents take actions in an environment in order to maximize a cumulative reward. In our novel implementation, each agent chooses a particular action from a fixed action set comprised of existing local enhancement operators to update each pixel intensity value. The pixel-wise arrangement of agents enables the algorithm to increase the resolution of an image by choosing optimal pixel intensity values from each option in a content-aware manner. While previous implementations of the model on SISR use Generative Adversarial Network (GAN)-based algorithms, we demonstrate that local operators can produce promising improved results without relying on the additional overhead of using machine learning techniques for the action space. Notably, we apply the proposed method to medical images, whereas previous implementations focused on natural images.

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Section: Introductionmentioning

confidence: 99%

Multi-step reinforcement learning for medical image super-resolution

Bouffard

Pop

Ebrahimi

2023

Medical Imaging 2023: Image Processing

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“…SR studies started before deep learning [34]. Advances in deep learning have affected SR studies and successful results have been observed [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Süper Çözünürlük Yönteminin Uydu İmgelerinin Sınıflandırma Performansına Etkisi

Cengiz

Avcı

2023

Turkish Journal of Science and Technology

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The high resolution of the image is very important for applications. Publicly available satellite images generally have low resolutions. Since low resolution causes loss of information, the desired performance cannot be achieved depending on the type of problem studied in the field of remote sensing. In such a case, super resolution algorithms are used to render low resolution images high resolution. Super resolution algorithms are used to obtain high resolution images from low resolution images. In studies with satellite images, the use of images enhanced with super resolution is important. Since the resolution of satellite images is low, the success rate in the classification process is low. In this study, super resolution method is proposed to increase the classification performance of satellite images. The attributes of satellite images were extracted using AlexNet, ResNet50, Vgg19 from deep learning architecture. Then the extracted features were then classified into 6 classes by giving input to AlexNet-Softmax, ResNet50-Softmax, Vgg19-Softmax, Support Vector Machine, K-Nearest Neighbor, decision trees and Naive Bayes classification algorithms. Without super resolution and with super resolution feature extraction and classification processes were performed separately. Classification results without super resolution and with super resolution were compared. Improvement in classification performance was observed using super resolution.

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“…Super-resolution techniques have revolutionized many fields such as biology, chemistry, and medicine, by enabling study on the order of a few nanometers [1][2][3]. These techniques overcome the inherent restriction of the diffraction limit due to the loss of evanescent waves that carry sub-wavelength information.…”

Section: Introductionmentioning

confidence: 99%

Geometrically flat hyperlens designed by transformation optics

Rho

2019

J. Phys. D: Appl. Phys.

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Here, we present a geometrically flat hyperlens that can still magnify and transfer subwavelength objects to the far-field. The flat hyperlens is designed using the transformation optics approach, which transforms the virtual space of the cylindrical coordinate into the physical space of the trapezoidal coordinate. We also suggest the possible design of two alternating multilayers by using an effective medium approximation. The sub-wavelength magnification using the designed flat hyperlens is numerically demonstrated. Such a flat hyperlens has great potential for practical application by solving many problems of conventional curvilinear hyperlenses arisen from their geometries.

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Super Resolution Imaging Applied to Scientific Images

Cited by 9 publications

References 27 publications

Multi-step reinforcement learning for medical image super-resolution

Multi-step reinforcement learning for medical image super-resolution

Süper Çözünürlük Yönteminin Uydu İmgelerinin Sınıflandırma Performansına Etkisi

Geometrically flat hyperlens designed by transformation optics

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