Improvement of image super-resolution reconstruction algorithm based on deep learning

At present, with the in-depth exploration of image super-resolution reconstruction technology promoted by deep learning, the model structure is becoming more and more complex, which leads to the reduction of the feature learning ability of the model, and the number of model parameters and calculation amount are greatly increased. Therefore, this paper proposes a combination of channel and spatial attention mechanism, residual feature fusion module, and improved knowledge distillation algorithm to enhance feature learning ability and reduce the number of redundant parameters in the network. The experimental results show that this method can effectively strengthen the feature learning ability of the model and reconstruct the high-frequency details, and realize the parameter compression of the model, and reduce the parameter calculation.