论文标题
基于注意机制和特征融合的图像超分辨率重建
Image super-resolution reconstruction based on attention mechanism and feature fusion
论文作者
论文摘要
旨在探索卷积神经网络忽略自然图像的固有属性和提取特征的问题，仅在图像超分辨率重建领域单个规模，提出了基于注意机制和多规模特征融合的网络结构。通过使用注意机制，网络可以有效地集成图像的非本地信息和二阶特征，从而提高网络的特征表达能力。同时，使用不同尺度的卷积内核来提取图像的多尺度信息，以便在不同尺度上保留完整的信息特征。实验结果表明，所提出的方法可以比其他代表性的超级分辨率重建算法在客观定量指标和视觉质量中实现更好的性能。
Aiming at the problems that the convolutional neural networks neglect to capture the inherent attributes of natural images and extract features only in a single scale in the field of image super-resolution reconstruction, a network structure based on attention mechanism and multi-scale feature fusion is proposed. By using the attention mechanism, the network can effectively integrate the non-local information and second-order features of the image, so as to improve the feature expression ability of the network. At the same time, the convolution kernel of different scales is used to extract the multi-scale information of the image, so as to preserve the complete information characteristics at different scales. Experimental results show that the proposed method can achieve better performance over other representative super-resolution reconstruction algorithms in objective quantitative metrics and visual quality.
