Abstract
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.
Highlights
Academic Editor: Wonho Jhe e 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. is network is constructed based on the residual dense network
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. is method can reduce the redundancy of network parameters and enhance the nonlinear mapping ability of the network at different scales. us, it can better learn and fit the feature information of the original image and recover the satisfactory super-resolution image
Summary
Researchers have witnessed the impressive performance of deep learning in single image super-resolution. Gao and Zhuang [26] developed a multiscale super-resolution method based on the deep neural network and showed the advantages of the multiscale residual dense network in feature extraction compared with the single-scale network. (2) Most of the existing models input the multiscale information from the previous multiscale feature extraction block directly into the later feature extraction block after fusion, which is likely to cause the problem of gradient vanishing To solve this problem, a multiscale dense residual network is proposed. A multiscale dense residual network is proposed In this method, the convolution kernel with different receptive fields is set up at different scales to integrate the advantages of multiscale, and the feature information of different receptive fields is extracted. Dilated convolution is used to expand the receptive field without changing the parameters of the convolution kernel, which reduces the scale of parameters compared with the direct use of different sizes of convolution kernel
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