基于注意力机制的残差特征聚合网络超分辨率图像重建研究

Study on Super-resolution Image Reconstruction Using Residual Feature Aggregation Network Based on Attention Mechanism

针对单图像超分辨率算法级联残差块的输出特征仅在局部作用的问题,提出了一种结合注意力机制的残差特征聚合网络。该网络通过跳跃连接将各残差块输出不同层次的特征聚合到残差组的尾部,实现特征的充分提取与复用,扩大网络的感受野并增强特征的表达能力,使得不同层次的特征图更充分地参与到图像重建中。同时,为增强特征信息空间上的相关性,引入增强空间注意力机制以改善残差块的性能。大量实验表明,此模型可以获得良好的超分辨率性能。在×4倍SR任务中与RCAN,SAN和HAN等主流方法相比,在5个基准测试集上取得的峰值信噪比平均提升0.07 dB,0.06 dB,0.006 dB,结构相似度平均提升0.0012,0.0011,0.0008,重建图像质量明显提高,细节更加丰富,充分说明了所提方法的有效性与先进性。

To address the problem of the local effect of the output features of cascaded residual blocks in single image super-resolution algorithms,a residual feature aggregation network combined with attention mechanism is proposed.The network aggregates the features of different levels output by each residual block through skip connections to the end of the residual group,achieves sufficient feature extraction and reuse,expands the receptive field of the network and enhances the expression ability of features.Meanwhile,to improve the spatial correlation of feature information,an enhanced spatial attention mechanism is introduced to improve the performance of the residual blocks.Extensive experiments demonstrate that the proposed model achieves good super-resolution performance.Compared with state-of-the-art methods such as RCAN,SAN,and HAN,the proposed method demonstrates significant effectiveness and advancement in the task of×4 super-resolution.On five benchmark datasets,our method achieves an average improvement of 0.07 dB,0.06 dB,and 0.006 dB in peak signal-to-noise ratio,as well as an average improvement of 0.0012,0.0011,and 0.0008 in structural similarity index.The reconstructed images exhibit a notable increase in quality,with more abundant details.These results verifies he efficacy and advancement of the proposed method.