关键词: 图像超分辨率重建, 特征提取, 多尺度残差注意块, 卷积注意块

Abstract:

In the process of image super-resolution reconstruction, there are some problems such as less image feature extraction, low information utilization, equal processing of high and low frequency information channels. The multi-scale residual attention block is constructed to maximize the network extraction of multi-dimensional feature information, and the channel attention mechanism is introduced to enhance the representation ability of high-frequency information channel. The convolution attention block feature extraction structure is introduced to reduce the loss of high-frequency image detail information. In the reconstruction layer of the network, the global long jump connection structure is introduced to further enrich the reconstruction high resolution image information flow. The experimental results show that the PSNR and SSIM of the proposed algorithm on Set5 and other benchmark datasets are significantly better than those of other deep convolutional neural networks, which verify the effectiveness and the advanced nature of the proposed method.

Key words: image super-resolution reconstruction, feature extraction, multi-scale residual attention block, convolution attention block