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

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

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(20), 2333;
Received: 4 September 2019 / Revised: 25 September 2019 / Accepted: 4 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Image Super-Resolution in Remote Sensing)
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. View Full-Text
Keywords: super-resolution; recursive neural network; dense connection; BiConvLSTM super-resolution; recursive neural network; dense connection; BiConvLSTM
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Chang, Y.; Luo, B. Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution. Remote Sens. 2019, 11, 2333.

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