Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration

Xu, Wenjia; Xu, Guangluan; Wang, Yang; Sun, Xian; Lin, Daoyu; Wu, Yirong

doi:10.3390/rs10121893

Cited by 26 publications

(18 citation statements)

References 42 publications

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“…Lei et al [37] propose a 'Multi-Fork' CNN architecture for training in an end-to-end manner. Xu et al [38] introduce a new deep memory connection network (DMCN), which reduces the time required to reconstruct the resolution of remote sensing images. Gu et al [39] use residual squeeze and excitation blocks to model the dependence among channels, which improves the representation ability.…”

Section: Remote Sensing Sr Methodsmentioning

confidence: 99%

Cross-Dimension Attention Guided Self-Supervised Remote Sensing Single-Image Super-Resolution

et al. 2021

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In recent years, the application of deep learning has achieved a huge leap in the performance of remote sensing image super-resolution (SR). However, most of the existing SR methods employ bicubic downsampling of high-resolution (HR) images to obtain low-resolution (LR) images and use the obtained LR and HR images as training pairs. This supervised method that uses ideal kernel (bicubic) downsampled images to train the network will significantly degrade performance when used in realistic LR remote sensing images, usually resulting in blurry images. The main reason is that the degradation process of real remote sensing images is more complicated. The training data cannot reflect the SR problem of real remote sensing images. Inspired by the self-supervised methods, this paper proposes a cross-dimension attention guided self-supervised remote sensing single-image super-resolution method (CASSISR). It does not require pre-training on a dataset, only utilizes the internal information reproducibility of a single image, and uses the lower-resolution image downsampled from the input image to train the cross-dimension attention network (CDAN). The cross-dimension attention module (CDAM) selectively captures more useful internal duplicate information by modeling the interdependence of channel and spatial features and jointly learning their weights. The proposed CASSISR adapts well to real remote sensing image SR tasks. A large number of experiments show that CASSISR has achieved superior performance to current state-of-the-art methods.

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Section: Remote Sensing Sr Methodsmentioning

confidence: 99%

Cross-Dimension Attention Guided Self-Supervised Remote Sensing Single-Image Super-Resolution

et al. 2021

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“…Finally, in [129], a novel deep memory-connected DNN with a large receptive field is introduced for remote sensing image restoration. Inspired by neuroscience concepts, the authors therein propose local and global memory connections to combine image detail information in lower layers with global information in higher layers of the network.…”

Section: Restorationmentioning

confidence: 99%

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Tsagkatakis

Aidini

Fotiadou

et al. 2019

Sensors

116

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Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

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“…DMCN is another CNN-based method, which proposed by Xu et al [33]. This method is used to handle various remote-sensing image restoration tasks such as SR and Gaussian denoising.…”

Section: Single Image Super-resolutionmentioning

confidence: 99%

Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks

Feng

Shen

et al. 2019

Remote Sensing

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Space object recognition is the basis of space attack and defense confrontation. High-quality space object images are very important for space object recognition. Because of the large number of cosmic rays in the space environment and the inadequacy of optical lenses and detectors on satellites to support high-resolution imaging, most of the images obtained are blurred and contain a lot of cosmic-ray noise. So, denoising methods and super-resolution methods are two effective ways to reconstruct high-quality space object images. However, most super-resolution methods could only reconstruct the lost details of low spatial resolution images, but could not remove noise. On the other hand, most denoising methods especially cosmic-ray denoising methods could not reconstruct high-resolution details. So in this paper, a deep convolutional neural network (CNN)-based single space object image denoising and super-resolution reconstruction method is presented. The noise is removed and the lost details of the low spatial resolution image are well reconstructed based on one very deep CNN-based network, which combines global residual learning and local residual learning. Based on a dataset of satellite images, experimental results demonstrate the feasibility of our proposed method in enhancing the spatial resolution and removing the noise of the space objects images.

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Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration

Cited by 26 publications

References 42 publications

Cross-Dimension Attention Guided Self-Supervised Remote Sensing Single-Image Super-Resolution

Cross-Dimension Attention Guided Self-Supervised Remote Sensing Single-Image Super-Resolution

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks

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