The current super-resolution methods cannot fully exploit the global and local information of the original low-resolution image, resulting in loss of some information. In order to solve the problem, we propose a multiscale residual dense network (MRDN) for image super-resolution. This network is constructed based on the residual dense network. It can integrate the multiscale information of the image and avoid losing too much information in the deep level of the network, while extracting more information under different receptive fields. In addition, in order to reduce the redundancy of the network parameters of MRDN, we further develop a lightweight parameter method and deploy it at different scales. This method can not only reduce the redundancy of network parameters but also enhance the nonlinear mapping ability of the network at different scales. Thus, it can better learn and fit the feature information of the original image and recover the satisfactory super-resolution image. Extensive experiments are conducted, which demonstrate the effectiveness of the proposed method.

中文翻译：

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使用轻量级多尺度残差密集网络的图像超分辨率

当前的超分辨率方法不能充分利用原始低分辨率图像的全局和局部信息，从而导致某些信息的丢失。为了解决该问题，我们提出了一种用于图像超分辨率的多尺度残差密集网络（MRDN）。该网络是基于残差密集网络构建的。它可以整合图像的多尺度信息，并避免在网络的深层丢失过多的信息，同时在不同的接收场下提取更多的信息。另外，为了减少MRDN网络参数的冗余性，我们进一步开发了一种轻量级的参数方法，并在不同规模下进行了部署。该方法不仅可以减少网络参数的冗余，而且可以增强网络在不同规模下的非线性映射能力。因此，它可以更好地学习和拟合原始图像的特征信息，并恢复令人满意的超分辨率图像。进行了广泛的实验，证明了该方法的有效性。