Multi-Frame Super-Resolution Algorithm Based on Attention Mechanism
Cheng Guo, Juhao Jiang, Qing Wang, Guannan Chen · 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP) · 2021
In the field of computer vision, image super-resolution is a difficult task, which have many applications in remote sensing, military and so on. In this paper, we introduce the convolutional block attention module (CBAM) into super-resolution problem, proposing a novel method called multi-frame super-resolution (MFSR) algorithm based on attention mechanism. Our proposed MFSR algorithm uses a three-layer CNN as benchmark and cascades CBAM at the end of each CNN block. The proposed algorithm can deliver a high-resolution output corresponding to the center (3rd) input frame. The average PSNR and SSIM of our algorithm are 33.318dB and 0.906 respectively, which outperform other MFSR algorithm.