论文标题
ASDN：任意比例图像超分辨率的深卷积网络
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution
论文作者
论文摘要
深度卷积神经网络已显着提高了超分辨率（SR）的峰值信噪比。但是，图像查看器应用程序通常允许用户将图像放大任意放大量表，迄今为止，以巨大的计算成本施加了大量必需的训练量表。为了获得任意规模SR的更高计算高效模型，本文采用Laplacian金字塔方法来使用Laplacian频率表示中的高频图像细节重建任何规模的高分辨率（HR）图像。对于小规模的SR（1至2之间），图像是通过稀疏的一组预先估计的Laplacian金字塔水平插值来构建的。较大尺度的SR是通过小规模的递归计算得出的，这大大降低了计算成本。为了进行全面比较，使用各种基准进行固定和任何规模的实验。在固定尺度上，ASDN优于预定的上采样方法（例如SRCNN，VDSR，DRRN）在PSNR中约为1 dB。在任何规模上，ASDN通常在许多尺度上都超过元SR。
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.
