ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution

• URL: http://arxiv.org/abs/2010.02414v1
• Date: Tue, 6 Oct 2020 01:18:46 GMT
• Title: ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution
• Authors: Jialiang Shen, Yucheng Wang, Jian Zhang
• Abstract summary: Image viewer applications commonly allow users to zoom the images to arbitrary magnification scales. This paper employs a Laplacian pyramid method to reconstruct any-scale high-resolution (HR) images using the high-frequency image details.
• Score: 6.672537252929814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks have significantly improved the peak signal-to-noise ratio of SuperResolution (SR). However, image viewer applications commonly allow users to zoom the images to arbitrary magnification scales, thus far imposing a large number of required training scales at a tremendous computational cost. To obtain a more computationally efficient model for arbitrary scale SR, this paper employs a Laplacian pyramid method to reconstruct any-scale high-resolution (HR) images using the high-frequency image details in a Laplacian Frequency Representation. For SR of small-scales (between 1 and 2), images are constructed by interpolation from a sparse set of precalculated Laplacian pyramid levels. SR of larger scales is computed by recursion from small scales, which significantly reduces the computational cost. For a full comparison, fixed- and any-scale experiments are conducted using various benchmarks. At fixed scales, ASDN outperforms predefined upsampling methods (e.g., SRCNN, VDSR, DRRN) by about 1 dB in PSNR. At any-scale, ASDN generally exceeds Meta-SR on many scales.

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