Image Super-resolution Networks based on Structure Reparameterized Convolution

Abstract

The traditional image super resolution method based on convolutional neural network generally brings two problems: First, the model has only a single scale of receptive field, which cannot use the image information in a smaller range; Second, the size of the feature map will become smaller and smaller in the process of continuous convolution, and it is necessary to continuously carry out edge zeroing operation to maintain the original size, which leads to the loss of part of the edge information. To solve the above two problems, we propose a network structure based on structure re-parameterized convolution, which sets a small convolution kernel in parallel beside a large convolution kernel in the same layer, trains the two cores simultaneously, and finally merges the two cores. The experimental results show that in this way, we make the large convolution kernel could capture smaller information, which not only improves the high-frequency details of the reconstructed image but also avoids frequent edge filling to reduce the information density, and effectively speeds up the reasoning speed after the model is re-parameterized. Compared with the advanced methods, our method achieves good results.