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A practical super-resolution method for multi-degradation remote sensing images with deep convolutional neural networks

A practical super-resolution method for multi-degradation remote sensing images with deep... Recent studies have proved that convolutional neural networks (CNNs) have great potential for image super-resolution (SR) tasks. However, most existing methods rely on paired high-resolution (HR) and low-resolution (LR) images to train the CNN, where the LR images are routinely synthesized by applying predefined degradation operations (e.g., bicubic). Because the degradation process of LR images is usually unknown and more complex than those predefined, these methods suffer a significant performance decrease when applied to real-world SR problems. In addition, a deeper and wider network structure enables superior performance while increasing the network parameters and inference time, making it difficult to process real-time data. Inspired by the above motivations, we present an efficient two-step SR method for multi-degradation remote sensing images. Specifically, we first present a novel kernel estimation framework based on generative adversarial networks that can accurately extract the latent blur kernel from the input LR image without any image priors. We then train an efficient SR deep neural network with paired HR and corresponding LR images degraded with the generated kernels. To better balance network parameters and network performance, the densely connected attention mechanism and multi-scale feature extract blocks are introduced in the SR network by increasing the flow of feature information within the network. Extensive experiments indicate that the proposed method outperforms current methods with desired network parameters and complexity, making it feasible to enable real-time image processing. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Real-Time Image Processing Springer Journals

A practical super-resolution method for multi-degradation remote sensing images with deep convolutional neural networks

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References (42)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1861-8200
eISSN
1861-8219
DOI
10.1007/s11554-022-01245-9
Publisher site
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Abstract

Recent studies have proved that convolutional neural networks (CNNs) have great potential for image super-resolution (SR) tasks. However, most existing methods rely on paired high-resolution (HR) and low-resolution (LR) images to train the CNN, where the LR images are routinely synthesized by applying predefined degradation operations (e.g., bicubic). Because the degradation process of LR images is usually unknown and more complex than those predefined, these methods suffer a significant performance decrease when applied to real-world SR problems. In addition, a deeper and wider network structure enables superior performance while increasing the network parameters and inference time, making it difficult to process real-time data. Inspired by the above motivations, we present an efficient two-step SR method for multi-degradation remote sensing images. Specifically, we first present a novel kernel estimation framework based on generative adversarial networks that can accurately extract the latent blur kernel from the input LR image without any image priors. We then train an efficient SR deep neural network with paired HR and corresponding LR images degraded with the generated kernels. To better balance network parameters and network performance, the densely connected attention mechanism and multi-scale feature extract blocks are introduced in the SR network by increasing the flow of feature information within the network. Extensive experiments indicate that the proposed method outperforms current methods with desired network parameters and complexity, making it feasible to enable real-time image processing.

Journal

Journal of Real-Time Image Processing – Springer Journals

Published: Dec 1, 2022

Keywords: Super-resolution; Kernel modeling; Multi-degradation; Remote sensing image; Convolutional neural network

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