Remote sensing images super-resolution with deep convolution networks

Ran, Qiong; Xing, Xu; Zhao, Shizhi; Li, Wei; Du, Qian

doi:10.1007/s11042-018-7091-1

Cited by 29 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Related Workmentioning

confidence: 99%

“…Super-resolution approaches have been employed for a wide variety of tasks, such as computer vision [23,24,8], remote sensing [7,25], face-related tasks [26,27] and medical applications [11,28]. Deep learning based methods have been widely used in recent times for performing super-resolution [29,30,24,25]. Moreover, deep learning based techniques have been proven to be a successful tool for numerous applications in the field of MRI, including for performing MR reconstruction [31,32,33,34] and for SR-MRI [12,35,36,13].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

Sarasaen,

Chatterjee,

Breitkopf

et al. 2021

Preprint

View full text Add to dashboard Cite

Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tuning to maximise spatial information while preserving high temporal resolution of dynamic MRI. An U-Net based network with perceptual loss is trained on a benchmark dataset and fine-tuned using one subject-specific static high resolution MRI as prior knowledge to obtain high resolution dynamic images during the inference stage. 3D dynamic data for three subjects were acquired with different parameters to test the generalisation capabilities of the network. The method was tested for different levels of in-plane undersampling for dynamic MRI. The reconstructed dynamic SR results showed higher similarity with the high resolution ground-truth after fine-tuning. The average SSIM of the lowest resolution experimented during this research (6.25 % of the k-space) before and after fine-tuning were 0.939 ± 0.008 and 0.957 ± 0.006 respectively. This could theoretically result in an acceleration factor of 16, which can potentially be acquired in less than half a second. The proposed approach shows that the super-resolution MRI reconstruction with prior-information can alleviate the spatio-temporal trade-off in dynamic MRI, even for high acceleration factors.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

Sarasaen,

Chatterjee,

Breitkopf

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Ran et al [80] consider a hierarchical CNN architecture which learns an end-to-end mapping from low-resolution to high-resolution images, by combining feature extraction and edge enhancement in the hierarchical layers. Extensions of this approach based on residual learning and multiscale version are also investigated for further improvements.…”

Section: Super-resolutionmentioning

confidence: 99%

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Tsagkatakis

Aidini

Fotiadou

et al. 2019

Sensors

116

View full text Add to dashboard Cite

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.

show abstract

“…Since there is a high correlation between the input image and the target HR image, many methods such as VDSR [2], EDSR [16], WDSR [17], SRResNet [25], SRDCN [26], and DRRN [27] use the local residual path from ResNet [24] and the global residual path to propagate the information from the shallow layer to the final reconstruction layer. Several methods based on DenseNet [27] like SRDenseNet [28] and RDN [29] use a concatenation strategy to combine preceding features to a bottleneck layer for reconstruction.…”

Section: Conv Conv Convmentioning

confidence: 99%

Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution

Chang

Luo

2019

Remote Sensing

View full text Add to dashboard Cite

Single-image super-resolution (SR) is an effective approach to enhance spatial resolution for numerous applications such as object detection and classification when the resolution of sensors is limited. Although deep convolutional neural networks (CNNs) proposed for this purpose in recent years have outperformed relatively shallow models, enormous parameters bring the risk of overfitting. In addition, due to the different scale of objects in images, the hierarchical features of deep CNN contain additional information for SR tasks, while most CNN models have not fully utilized these features. In this paper, we proposed a deep yet concise network to address these problems. Our network consists of two main structures: (1) recursive inference block based on dense connection reuse of local low-level features, and recursive learning is applied to control the model parameters while increasing the receptive fields; (2) a bidirectional convolutional LSTM (BiConvLSTM) layer is introduced to learn the correlations of features from each recursion and adaptively select the complementary information for the reconstruction layer. Experiments on multispectral satellite images, panchromatic satellite images, and nature high-resolution remote-sensing images showed that our proposed model outperformed state-of-the-art methods while utilizing fewer parameters, and ablation studies demonstrated the effectiveness of a BiConvLSTM layer for an image SR task.

show abstract

Remote sensing images super-resolution with deep convolution networks

Cited by 29 publications

References 29 publications

Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution

Contact Info

Product

Resources

About