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Super-Resolution using Sub-pixel Recursive Adversarial Network with Perceptual Loss

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Ganesh Singh Bisht, Pawan Kumar Mishra
10.5120/ijca2017915273

Ganesh Singh Bisht and Pawan Kumar Mishra. Super-Resolution using Sub-pixel Recursive Adversarial Network with Perceptual Loss. International Journal of Computer Applications 173(3):28-34, September 2017. BibTeX

@article{10.5120/ijca2017915273,
	author = {Ganesh Singh Bisht and Pawan Kumar Mishra},
	title = {Super-Resolution using Sub-pixel Recursive Adversarial Network with Perceptual Loss},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2017},
	volume = {173},
	number = {3},
	month = {Sep},
	year = {2017},
	issn = {0975-8887},
	pages = {28-34},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume173/number3/28317-2017915273},
	doi = {10.5120/ijca2017915273},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Super-Resolution is a classic problem in computer vision and many methods have been designed to reconstruct the high-resolution image from low-resolution image. Recent solution of such problem is based on the convolutional neural network where mapping function is used to map low-resolution image to high-resolution image based on per-pixel loss or mean-square error. We introduce a framework that uses perceptual loss function and provides much finer results with high improvement in speed. This framework also replaces the bicubic interpolation for upscaling image with the sub-pixel convolutional layer that learns upscaling filters to upscale the low-resolution feature map to the high-resolution image, that leads less computational complexity. The Proposed method also deals with high upscale factor, by the introduction of an adversarial network that helps in recovering finer texture details in a low-resolution image.

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Keywords

Super-resolution, deep learning, convolutional neural network, perceptual loss, adversarial network, sub-pixel convolutional layer.