Dynamic Convolutional Neural Network For Image Super-resolution

Authors

  • Anil Bhujel Ministry of Information and Communication, Singhdurbar, Kathmandu
  • Dibakar Raj Pant Department of Electronic and Computer Engineering, Pulchowk Campus, Pulchowk, Lalitpur

DOI:

https://doi.org/10.3126/jacem.v3i0.18808

Keywords:

Super-resolution, Convolutional Neural Network, Dynamic convolutional neural network

Abstract

Single image super-resolution (SISR) is a technique that reconstructs high resolution image from single low resolution image. Dynamic Convolutional Neural Network (DCNN) is used here for the reconstruction of high resolution image from single low resolution image. It takes low resolution image as input and produce high resolution image as output for dynamic up-scaling factor 2, 3, and 4. The dynamic convolutional neural network directly learns an end-to-end mapping between low resolution and high resolution images. The CNN trained simultaneously with images up-scaled by factors 2, 3, and 4 to make it dynamic. The system is then tested for the input images with up-scaling factors 2, 3 and 4. The dynamically trained CNN performs well for all three up-scaling factors. The performance of network is measured by PSNR, WPSNR, SSIM, MSSSIM, and also by perceptual.

Journal of Advanced College of Engineering and Management, Vol. 3, 2017, Page: 1-10

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Published

2018-01-10

How to Cite

Bhujel, A., & Pant, D. R. (2018). Dynamic Convolutional Neural Network For Image Super-resolution. Journal of Advanced College of Engineering and Management, 3, 1–10. https://doi.org/10.3126/jacem.v3i0.18808

Issue

Vol. 3 (2017)

Section

Articles

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