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Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units.
Cubic convolution interpolation for digital image processing
Robert Keys · 1981
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Bilinear interpolation
Earl J Kirkland and Earl J Kirkland · 2010
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Deep sparse rectifier neural networks
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi-Morel · 2012
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Anchored neighborhood regression for fast example-based super-resolution
Radu Timofte, Vincent De Smet, and Luc Van Gool · 2013
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Adam: A method for stochastic optimization
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Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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Fast and memory-efficient network towards efficient image super-resolution
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Mulut: Cooperating multiple look-up tables for efficient image super-resolution
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Reconstructed convolution module based look-up tables for efficient image super-resolution
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