Abstract

We present a single-image super-resolution (SR) method for Remote Sensing Image based on deep learning within Discrete Wavelet Domain in this paper. Our method is inspired Residual Learning. Firstly, an input image is decomposed by single level 2D Discrete wavelet transform to get four sub-bands. The four sub-bands coefficients are feeding into the Deep Learning Residual Network to predict correspondingly residual images; Adding four sub-band images and residual images as the new sub-bands of 2D wavelet transform; Finally, uses the inverse 2D Discrete wavelet transform to get the final output Super Resolution HR image. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.

中文翻译：

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离散小波域中基于深度ResNet的遥感图像超分辨率重构

摘要

本文提出了一种基于离散小波域内深度学习的遥感图像单图像超分辨率（SR）方法。我们的方法是启发式残余学习。首先，通过单级二维离散小波变换对输入图像进行分解，得到四个子带。四个子带系数被馈入深度学习残差网络，以预测相应的残差图像。将四个子带图像和残差图像相加作为二维小波变换的新子带；最后，使用逆二维离散小波变换获得最终输出的超分辨率HR图像。我们提出的方法在准确性上要比现有方法更好，并且视觉效果在视觉上也很明显。