Convolutional neural networks (CNNs) have recently made impressive results for image super-resolution (SR). Our goal is to introduce a new image SR framework rely on a CNN. In this paper, the input image is decomposed into luminance channel and chromatic channels. A designed network based on a residual dense network is introduced to extract the hierarchical features from luminance part. The bicubic interpolation is simply used to upscale low resolution (LR) chromatic channels. However, this step degrades the chromatic channels. To tackle this issue, the SR reconstructed luminance channel is applied as the reference image in guided filters to promote the interpolated chromatic channels. Guided filters technique has ability to retain sharp edges and fine details from the reference image and carry them to the target images. Extensive experiments on several commonly used image SR testing datasets demonstrate that our framework has the ability to extract features and outperforms existing well-known techniques for image SR by LR image into the high resolution (HR) image efficiently.

卷积神经网络（CNN）最近在图像超分辨率（SR）方面取得了令人印象深刻的结果。我们的目标是引入一种基于CNN的新图像SR框架。在本文中，输入图像被分解为亮度通道和色度通道。引入了一种基于残差密集网络的设计网络，以从亮度部分提取层次特征。双三次插值仅用于升级低分辨率（LR）色度通道。但是，此步骤会使彩色通道降级。为了解决该问题，将SR重构的亮度通道用作引导滤波器中的参考图像以促进内插的色通道。引导滤镜技术能够保留参考图像的锐利边缘和精细细节，并将其带到目标图像。