A Technical Review on Image Super-Resolution Techniques

Shukla, Anugya; Merugu, Suresh; Jain, Kamal

doi:10.1007/978-981-15-3125-5_54

Cited by 13 publications

(10 citation statements)

References 30 publications

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“…High-resolution photographs may provide more detailed information to everyone, making them more useful in a variety of applications such as satellite imagery, medical images, and so on. Several approaches in the field of sophisticated color digital image processing have emerged as a result of the increased technological interest in picture reconstruction (Shukla et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Survey on CNN based super resolution methods

Kazem

Suad

Abdulbaqi

2021

JLMA

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Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.

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Section: Introductionmentioning

confidence: 99%

Survey on CNN based super resolution methods

Kazem

Suad

Abdulbaqi

2021

JLMA

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“…However, scientific images have a significantly larger dynamic range than natural images (each image pixel can assume real values spanning several orders of magnitude) and the generation of perceptually correct images is not a sufficient outcome because their use typically involves precise numerical calculations that are quantitatively more sensitive than measuring perceptual quality. Our work has three main objectives: (1) to demonstrate that a deep learning approach can leverage the information present in astronomical images with the goal of adding detail to low resolution images while maintaining their scientific accuracy; (2) to show how super-resolving a scientific image via deep learning also homogenizes instrument systematic properties, (3) to establish a set of quantitative performance measurements that can be used to benchmark the performance of different super-resolution algorithms for astronomical images, as well as to benchmark the performance of future applications of super-resolution to the physical sciences.…”

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confidence: 99%

“…To compare with HMI, MDI and GONG images are upsampled as described in section 2.1. The Pearson correlation coefficient is computed as in(1)…”

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confidence: 99%

“…Super-resolution (SR) is an image processing technique that aims to increase the resolution of an image by adding sub-pixel detail. 1 The information used for adding detail can come from sub-pixel shifts provided by sequences of images (frequency domain), or by a good understanding of the degradation processes, including blurring, that cause the loss of detail (i.e., atmospheric seeing, point spread function, etc.) 1 In the case of applications with sufficient and representative low (LR) and high-resolution (HR) samples, context can provide an additional source of information (i.e., the knowledge that all LR images belong to a specific category).…”

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confidence: 99%

“…1 The information used for adding detail can come from sub-pixel shifts provided by sequences of images (frequency domain), or by a good understanding of the degradation processes, including blurring, that cause the loss of detail (i.e., atmospheric seeing, point spread function, etc.) 1 In the case of applications with sufficient and representative low (LR) and high-resolution (HR) samples, context can provide an additional source of information (i.e., the knowledge that all LR images belong to a specific category). convolutional neural networks (CNNs) are especially suited for this type of application due to their ability to empirically map the underlying connections between an image pixel and those surrounding it.…”

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confidence: 99%

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Physics informed deep learning to super-resolve and cross-calibrate solar magnetograms

Muñoz-Jaramillo

Jungbluth

Gitiaux

et al. 2021

Preprint

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Super-resolution techniques aim to increase the resolution of images by adding detail. Compared to upsampling techniques reliant on interpolation, deep learning-based approaches learn features and their relationships across the training data set to leverage prior knowledge on what low resolution patterns look like in higher resolution images. As an added benefit, deep neural networks can learn the systematic properties of the target images (i.e.\ texture), combining super-resolution with instrument cross-calibration. While the successful use of super-resolution algorithms for natural images is rooted in creating perceptually convincing results, super-resolution applied to scientific data requires careful quantitative evaluation of performances. In this work, we demonstrate that deep learning can increase the resolution and calibrate space- and ground-based imagers belonging to different instrumental generations. In addition, we establish a set of measurements to benchmark the performance of scientific applications of deep learning-based super-resolution and calibration. We super-resolve and calibrate solar magnetic field images taken by the Michelson Doppler Imager (MDI; resolution ~2"/pixel; science-grade, space-based) and the Global Oscillation Network Group (GONG; resolution ~2.5"/pixel; space weather operations, ground-based) to the pixel resolution of images taken by the Helioseismic and Magnetic Imager (HMI; resolution ~0.5"/pixel; last generation, science-grade, space-based).

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