scholarly journals Spectral recovery‐guided hyperspectral super‐resolution using transfer learning

2021 ◽  
Author(s):  
Shaolei Zhang ◽  
Guangyuan Fu ◽  
Hongqiao Wang ◽  
Yuqing Zhao
Keyword(s):  
Transfer Learning ◽  
Super Resolution ◽  
Spectral Recovery

scholarly journals SURFNet: Super-Resolution of Turbulent Flows with Transfer Learning using Small Datasets

2021 ◽  
Author(s):  
Octavi Obiols-Sales ◽  
Abhinav Vishnu ◽  
Nicholas P. Malaya ◽  
Aparna Chandramowlishwaran
Keyword(s):  
Turbulent Flows ◽  
Transfer Learning ◽  
Super Resolution

scholarly journals Super-Resolution of Cardiac MR Cine Imaging using Conditional GANs and Unsupervised Transfer Learning

2021 ◽  
pp. 102037
Author(s):  
Yan Xia ◽  
Nishant Ravikumar ◽  
John P. Greenwood ◽  
Stefan Neubauer ◽  
Steffen E. Petersen ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Super Resolution ◽  
Cine Imaging ◽  
Cardiac Mr

scholarly journals Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
YiNan Zhang ◽  
MingQiang An
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Medical Images ◽  
Super Resolution ◽  
Training Dataset ◽  
Reconstruction Method ◽  
Hybrid Architecture ◽  
Learning Approaches ◽  
Training Samples ◽  
Bicubic Interpolation

Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template layer and two convolutional layers. The bicubic interpolation template layer is prefixed by mathematics deduction, and two convolutional layers learn from training samples. For saving training medical images, a SIFT feature-based transfer learning method is proposed. Not only can medical images be used to train the proposed method, but also other types of images can be added into training dataset selectively. In empirical experiments, results of eight distinctive medical images show improvement of image quality and time reduction. Further, the proposed method also produces slightly sharper edges than other deep learning approaches in less time and it is projected that the hybrid architecture of prefixed template layer and unfixed hidden layers has potentials in other applications.

Deep gradient prior network for DEM super-resolution: Transfer learning from image to DEM

2019 ◽  
Vol 150 ◽  
pp. 80-90 ◽  
Author(s):  
Zekai Xu ◽  
Zixuan Chen ◽  
Weiwei Yi ◽  
Qiuling Gui ◽  
Wenguang Hou ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Super Resolution

scholarly journals Infrared Image Super-Resolution via Progressive Compact Distillation Network

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3107
Author(s):  
Kefeng Fan ◽  
Kai Hong ◽  
Fei Li
Keyword(s):  
Transfer Learning ◽  
Learning Strategy ◽  
Infrared Image ◽  
Super Resolution ◽  
Infrared Images ◽  
Deep Convolutional Neural Networks ◽  
Calculation Algorithm ◽  
Reconstruction Performance ◽  
Image Super Resolution ◽  
Public Datasets

Deep convolutional neural networks are capable of achieving remarkable performance in single-image super-resolution (SISR). However, due to the weak availability of infrared images, heavy network architectures for insufficient infrared images are confronted by excessive parameters and computational complexity. To address these issues, we propose a lightweight progressive compact distillation network (PCDN) with a transfer learning strategy to achieve infrared image super-resolution reconstruction with a few samples. We design a progressive feature residual distillation (PFDB) block to efficiently refine hierarchical features, and parallel dilation convolutions are utilized to expand PFDB’s receptive field, thereby maximizing the characterization power of marginal features and minimizing the network parameters. Moreover, the bil-global connection mechanism and the difference calculation algorithm between two adjacent PFDBs are proposed to accelerate the network convergence and extract the high-frequency information, respectively. Furthermore, we introduce transfer learning to fine-tune network weights with few-shot infrared images to obtain infrared image mapping information. Experimental results suggest the effectiveness and superiority of the proposed framework with low computational load in infrared image super-resolution. Notably, our PCDN outperforms existing methods on two public datasets for both ×2 and ×4 with parameters less than 240 k, proving its efficient and excellent reconstruction performance.

scholarly journals Domain Transfer Learning for Hyperspectral Image Super-Resolution

2019 ◽  
Vol 11 (6) ◽  
pp. 694 ◽  
Author(s):  
Xiaoyan Li ◽  
Lefei Zhang ◽  
Jane You
Keyword(s):  
High Resolution ◽  
Transfer Learning ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Image Super Resolution ◽  
Domain Transfer ◽  
Domain Transfer Learning

A Hyperspectral Image (HSI) contains a great number of spectral bands for each pixel; however, the spatial resolution of HSI is low. Hyperspectral image super-resolution is effective to enhance the spatial resolution while preserving the high-spectral-resolution by software techniques. Recently, the existing methods have been presented to fuse HSI and Multispectral Images (MSI) by assuming that the MSI of the same scene is required with the observed HSI, which limits the super-resolution reconstruction quality. In this paper, a new framework based on domain transfer learning for HSI super-resolution is proposed to enhance the spatial resolution of HSI by learning the knowledge from the general purpose optical images (natural scene images) and exploiting the cross-correlation between the observed low-resolution HSI and high-resolution MSI. First, the relationship between low- and high-resolution images is learned by a single convolutional super-resolution network and then is transferred to HSI by the idea of transfer learning. Second, the obtained Pre-high-resolution HSI (pre-HSI), the observed low-resolution HSI, and high-resolution MSI are simultaneously considered to estimate the endmember matrix and the abundance code for learning the spectral characteristic. Experimental results on ground-based and remote sensing datasets demonstrate that the proposed method achieves comparable performance and outperforms the existing HSI super-resolution methods.

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