Abstract

Advent of convolutional neural network (CNN) in the field of single image super-resolution (SISR) has shown immense improvement in the process of high resolution (HR) image generation. It involves an end-to-end mathematical mapping through non-linear feature extraction between low resolution (LR) and HR image. Performance of CNN can be improved by increasing depth of the architecture, which generally results in higher computational cost and running time. Also, the performance of CNN can be improved by providing more appropriate input. Presently, in most of the cases input image to a CNN is a LR image that is bi-cubically interpolated to the desired size of HR image. However, bicubic interpolation results into detail smoothing of the image. Therefore, in this paper, a hybrid of CNN and fractal interpolation based SISR algorithm is proposed for reconstruction of HR image. Here, a three layered light-weight CNN architecture is utilize which is capable of producing comparable performance with the traditional SISR techniques and fractal interpolation helps in better preservation of structural and textural properties of the HR image. Experimental results are provided to prove the efficacy of the algorithm proposed in the paper.

中文翻译：

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分形插值和卷积神经网络的混合单图像超分辨率技术

摘要

在单图像超分辨率（SISR）领域中，卷积神经网络（CNN）的出现显示了高分辨率（HR）图像生成过程中的巨大进步。它涉及通过低分辨率（LR）和HR图像之间的非线性特征提取进行的端到端数学映射。可以通过增加体系结构的深度来提高CNN的性能，这通常会导致更高的计算成本和运行时间。同样，可以通过提供更适当的输入来改善CNN的性能。当前，在大多数情况下，到CNN的输入图像是LR图像，该图像被双三次内插到HR图像的所需大小。但是，三次三次插值会导致图像的细节平滑。因此，在本文中，提出了一种基于CNN和分形插值的SISR混合算法来重建HR图像。在这里，采用了三层轻型CNN架构，该架构能够产生与传统SISR技术相当的性能，而分形插值有助于更好地保留HR图像的结构和纹理特性。实验结果证明了该算法的有效性。