Super-resolution of wide-field infrared and low light level images using convolutional networks

Yu-dan Chen; Gang Li; Fu-yu Huang; He Liu

doi:10.1117/12.2606565

24 November 2021 Super-resolution of wide-field infrared and low light level images using convolutional networks

Yu-dan Chen, Gang Li, Fu-yu Huang, He Liu

Proceedings Volume 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations; 120690R (2021) https://doi.org/10.1117/12.2606565
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

SRCNN firstly applies convolutional neural network to image super-resolution reconstruction, which is the most representative method of super-resolution reconstruction algorithm based on deep learning. In this paper, SRCNN was taken as an example to discuss the application of deep learning method in the super-resolution reconstruction of Wide-Field Infrared and Low Light Level Images. The main work was to compare the effects of training data sets on reconstruction results. Two kinds of data sets were used to train SRCNN. Model 1 used 91 ordinary natural images as training data set, and model 2 used 29 ordinary natural images and 62 Wide-Field images as training data sets. Two groups of Wide-Field Infrared and Low Light Level Images were tested by using the models trained from the two datasets, and the PNSR and SSIM parameters of the test results were compared.

Citation Download Citation

Yu-dan Chen, Gang Li, Fu-yu Huang, and He Liu "Super-resolution of wide-field infrared and low light level images using convolutional networks", Proc. SPIE 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations, 120690R (24 November 2021); https://doi.org/10.1117/12.2606565

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