Abstract:
Single Image Super-Resolution (SISR) is looking at restoring the missing high-resolution information from a single low-resolution image in order to increase the apparent ...Show MoreMetadata
Abstract:
Single Image Super-Resolution (SISR) is looking at restoring the missing high-resolution information from a single low-resolution image in order to increase the apparent spatial resolution by a factor of two or more. In recent years, convolution neural networks have been applied with great success to the problem of improving spatial resolution from a single image. With the advent of low-resolution (10 m) optical sensors such as Sentinel-2, it is interesting to explore the possibility of improving image resolution with Deep Learning (DL) techniques. The purpose of this article is to investigate the potential performances of recent DL super-resolution techniques. The techniques explored here include not only techniques for enhancing high-frequency content but also so-called image-to-image translation techniques based on Generative Adversarial Neural Networks (GAN). From our preliminary results, we show that GANs have the ability to restore complex textural information.
Date of Conference: 22-27 July 2018
Date Added to IEEE Xplore: 04 November 2018
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ISSN Information:
Cornputer Research Institute of Montreal, Montreal, Quebec, Canada
Cornputer Research Institute of Montreal, Montreal, Quebec, Canada
Local Logic inc., Montreal, Canada
Local Logic inc., Montreal, Canada
Cornputer Research Institute of Montreal, Montreal, Quebec, Canada
Cornputer Research Institute of Montreal, Montreal, Quebec, Canada
Local Logic inc., Montreal, Canada
Local Logic inc., Montreal, Canada