Spatial Resolution Enhancement of Satellite Microwave Radiometer Data with Deep Residual Convolutional Neural Network

Satellite microwave radiometer data is affected by many degradation factors during the imaging process, such as the sampling interval, antenna pattern and scan mode, etc., leading to spatial resolution reduction. In this paper, a deep residual convolutional neural network (CNN) is proposed to solve these degradation problems by learning the end-to-end mapping between low-and high-resolution images. Unlike traditional methods that handle each degradation factor separately, our network jointly learns both the sampling interval limitation and the comprehensive degeneration factors, including the antenna pattern, receiver sensitivity and scan mode, during the training process. Moreover, due to the powerful mapping capability of the deep residual CNN, our method achieves better resolution enhancement results both quantitatively and qualitatively than the methods in literature. The microwave radiation imager (MWRI) data from the Fengyun-3C (FY-3C) satellite has been used to demonstrate the validity and the effectiveness of the method.

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Simultaneous denoising and spatial resolution enhancement using convolutional neural network-based linear model in diagnostic CT images

Medical Imaging 2020: Physics of Medical Imaging ◽

10.1117/12.2548378 ◽

2020 ◽

Author(s):

Dobin Yim ◽

Burnyoung Kim ◽

Seungwan Lee

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Linear Model ◽

Spatial Resolution ◽

Resolution Enhancement ◽

Ct Images ◽

Diagnostic Ct

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Spatial Resolution Matching of Microwave Radiometer Data with Convolutional Neural Network

Remote Sensing ◽

10.3390/rs11202432 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2432 ◽

Cited By ~ 2

Author(s):

Yade Li ◽

Weidong Hu ◽

Shi Chen ◽

Wenlong Zhang ◽

Rui Guo ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Microwave Radiation ◽

Spatial Resolution ◽

State Of The Art ◽

Microwave Radiometer ◽

Microwave Remote Sensing ◽

Degradation Model ◽

Matching Problems

Passive multi-frequency microwave remote sensing is often plagued with the problems of low- and non-uniform spatial resolution. In order to adaptively enhance and match the spatial resolution, an accommodative spatial resolution matching (ASRM) framework, composed of the flexible degradation model, the deep residual convolutional neural network (CNN), and the adaptive feature modification (AdaFM) layers, is proposed in this paper. More specifically, a flexible degradation model, based on the imaging process of the microwave radiometer, is firstly proposed to generate suitable datasets for various levels of matching tasks. Secondly, a deep residual CNN is introduced to jointly learn the complicated degradation factors of the data, so that the resolution can be matched up to fixed levels with state of the art quality. Finally, the AdaFM layers are added to the network in order to handle arbitrary and continuous resolution matching problems between a start and an end level. Both the simulated and the microwave radiation imager (MWRI) data from the Fengyun-3C (FY-3C) satellite have been used to demonstrate the validity and the effectiveness of the method.

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Spatial Resolution Enhancement of Brillouin Optical Correlation-Domain Reflectometry Using Convolutional Neural Network: Proof of Concept

IEEE Access ◽

10.1109/access.2021.3110874 ◽

2021 ◽

pp. 1-1

Author(s):

Jelah N. Caceres ◽

Kohei Noda ◽

Guangtao Zhu ◽

Heeyoung Lee ◽

Kentaro Nakamura ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Spatial Resolution ◽

Resolution Enhancement ◽

Proof Of Concept ◽

Optical Correlation

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Cascaded Convolutional Neural Network-Based Hyperspectral Image Resolution Enhancement via an Auxiliary Panchromatic Image

IEEE Transactions on Image Processing ◽

10.1109/tip.2021.3098246 ◽

2021 ◽

pp. 1-1

Author(s):

Xiaochen Lu ◽

Junping Zhang ◽

Dezheng Yang ◽

Longting Xu ◽

Feng De Jia

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Hyperspectral Image ◽

Resolution Enhancement ◽

Image Resolution ◽

Panchromatic Image

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Predicting placenta transcriptional regulatory interactions based on immunohistochemistry images data and convolutional neural network

10.1101/2020.12.18.423383 ◽

2020 ◽

Author(s):

Yajun Liu ◽

Yilin Guo ◽

Ya Gao ◽

Guiming Hu ◽

Ju Ma ◽

...

Keyword(s):

Neural Network ◽

High Resolution ◽

Convolutional Neural Network ◽

Human Placenta ◽

Chorionic Villus ◽

Gene Interaction ◽

Gene Interaction Networks ◽

Transcriptional Regulatory ◽

High Resolution Images

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, immunohistochemistry images of different TFs in chorionic villus were obtained by a high-resolution scanner. Next, we used a convolutional neural network and machine learning method to infer gene interaction networks of human placenta from these images based on the transfer learning technique. The experimental results show that deep learning models reveals regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. Conclusions: We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

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