A Taxonomy of Example-Based Super Resolution

Salvador, Jordi

doi:10.1016/b978-0-12-809703-8.00002-2

Cited by 13 publications

(11 citation statements)

References 58 publications

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“…One of the fields where this benefit is exploited is the example-based super resolution [56,57] in computer science. Through the use of super-resolution reconstruction, a high-resolution signal can be reconstructed from a given low-resolution input.…”

Section: Super-resolution Analysis Of Flow Image Datamentioning

confidence: 99%

Assessment of supervised machine learning methods for fluid flows

Fukami

Fukagata

Taira

2020

Theor. Comput. Fluid Dyn.

193

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We apply supervised machine learning techniques to a number of regression problems in fluid dynamics. Four machine learning architectures are examined in terms of their characteristics, accuracy, computational cost, and robustness for canonical flow problems. We consider the estimation of force coefficients and wakes from a limited number of sensors on the surface for flows over a cylinder and NACA0012 airfoil with a Gurney flap. The influence of the temporal density of the training data is also examined. Furthermore, we consider the use of convolutional neural network in the context of super-resolution analysis of two-dimensional cylinder wake, two-dimensional decaying isotropic turbulence, and three-dimensional turbulent channel flow. In the concluding remarks, we summarize on findings from a range of regression type problems considered herein.

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Section: Super-resolution Analysis Of Flow Image Datamentioning

confidence: 99%

Assessment of supervised machine learning methods for fluid flows

Fukami

Fukagata

Taira

2020

Theor. Comput. Fluid Dyn.

193

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“…This approach predicts the missing high frequency information that does not exist in the LR image and which cannot be reconstructed by simple image interpolation [44]. SISR is more general than MISR because there are fundamental limits to MISR, such as poor registration, low resolution gain, too much noise, and slow processing speed that deter MISR approach to provide higher resolutions [45].…”

Section: Traditional Super Resolution Methodsmentioning

confidence: 99%

“…Parametric methods create the super resolution image via mapping functions, controlled by a compact number of parameters, learned from examples that do not necessarily come from the input image. One possible solution is to adapt the interpolation algorithm to the local covariances images [45].…”

Section: Example-based Super Resolutionmentioning

confidence: 99%

Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

et al. 2021

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Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.

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“…A more successful alternative to interpolation is example-based methods. 4 These methods include machine learning algorithms, such as Neighbour Embedding 5678 , Sparse Coding 91011 , Regression Trees 121314 , and Deep Learning (DL). DL is a sub-field of machine learning, which is in turn a sub-field of Artificial Intelligence.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution of satellite imagery using a wavelet multiscale-based deep convolutional neural network model

Aburaed

Panthakkan

Al-Saad

et al. 2020

Image and Signal Processing for Remote Sensing XXVI

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Nowadays, satellite images are used in various governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. As such, increasing the spatial resolution of an image is an important pre-processing step that can improve the performance of various image processing tasks, such as segmentation. Once a satellite is launched, the more practical solution to improve the resolution of its captured images is to use Single Image Super Resolution (SISR) techniques. In the recent years, Deep Convolutional Neural Networks (DCNNs) have been recognized as a highly effective tool to reconstruct a High Resolution (HR) image from its Low Resolution (LR) counterpart, which is an open problem due to the inherent difficulty of estimating the missing high frequency components. The aim of this research paper is to design and implement a satellite image SISR algorithm by estimating high frequency details through training Deep Convolutional Neural Network (DCNNs) with respect to wavelet analysis. The goal is to improve the spatial resolution of multispectral remote sensing images captured by DubaiSat-2 satellite. The accuracy of the proposed algorithm is assessed using several metrics such as Peak Signal-to-Noise Ratio (PSNR), Wavelet-based Signal-to-Noise Ratio (WSNR) and Structural Similarity Index Measurement (SSIM).

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A Taxonomy of Example-Based Super Resolution

Cited by 13 publications

References 58 publications

Assessment of supervised machine learning methods for fluid flows

Assessment of supervised machine learning methods for fluid flows

Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

Super-resolution of satellite imagery using a wavelet multiscale-based deep convolutional neural network model

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