Super-resolution technique is an application of estimating high-resolution images from low-resolution images. Although significant progress in this field has been achieved by deep neural network,there are still some problems,such as sensitivity to small structural changes. Therefore,residual dense network is applied to improve the flexibility of GAN network;in addition,for the application of fractional order calculus in the field of neural network,the FoMCA fractional order multi-channel attention mechanism module is proposed,which improves the performance and efficiency of the model through the design of new order adjustment function,which provides a new way of thinking for constructing and training of deeper trainable network;finally,comparison experiment and ablation experiment are carried out to verify effectiveness of the above methods.
关键词
分数阶微积分/注意力机制/残差密集网络/神经网络
Key words
Fractional order calculus/attention mechanism/residual dense network/neural network
维普
万方数据