Abstract:

Some super-resolution networks ignore the reuse of the features of different levels, and there is no fusing of the features. In order to solve those problems, a second-order progressive feature fusion super-resolution network with strong feature reuse and fuse ability is constructed to realize the reconstructed image with high resolution and high fidelity. The core of the network is progressive feature fusion block. Progressive feature fusion block enhances the reuse of features through feature fusion operation. In addition, a second-order feature fusion mechanism is proposed, which adopts progressive feature fusion method for feature fusion at the local and global levels of the network. The experimental results show that the reconstructed image of the network is clearer than that of other networks on line and contour, and better results are obtained in peak signal to noise ratio (SNR) and structural similarity. For example, when the scaling factor is 2, the peak SNR/structure similarity on each test set is 38.20 dB/0.961 2, 33.81 dB/0.919 5, 32.28 dB/0.901 0, 32.65 dB/0.932 4, and 39.11 dB/0.977 9 respectively, which proves that the proposed model acheives improvent compared to other models. The advantages of the second-order progressive feature fusion super-resolution network is proven from the objective standard and subjective point of view.

Key words: super-resolution reconstruction, convolutional neural network (CNN), feature fusion, second-order feature fusion mechanism