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Proceedings Paper

A super-resolution reconstruction method for remote sensing images based on Adam optimized depth convolution network
Author(s): Yanqin Jia
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Paper Abstract

Super-resolution reconstructed convolution neural network (SRCNN) is widely used in image quality improvement of single image. Traditional SRCNN training uses the loss function of minimum mean square error (MSE) and the method based on stochastic gradient descent (SGD) to optimize. Its learning rate adjustment strategy is limited by pre-specified adjustment rules, and it is difficult to select the initial value. Considering the complex texture and low resolution of remote sensing images, a deconvolution layer is proposed to replace the bi-cubic interpolation enlarged image in the traditional SRCNN network to overcome the mosaic effect. At the same time, Adam optimizer is used to control the network training. After considering the first and second moment estimation of gradient comprehensively, the update step is calculated. Thus, the adaptive update of learning rate is realized and the speed of network training is greatly accelerated. The simulation results show that this method has advantages in edge reconstruction and texture details compared with the conventional super-resolution reconstruction algorithm.

Paper Details

Date Published: 12 March 2020
PDF: 8 pages
Proc. SPIE 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 1143809 (12 March 2020);
Show Author Affiliations
Yanqin Jia, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 11438:
2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology
Guohai Situ; Xun Cao; Wolfgang Osten, Editor(s)

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