基于特征图注意力机制的图像超分辨率重建

Image Super-Resolution Reconstruction Based on Attention Mechanism of Feature Map

图像超分辨率重建中的高频分量通常包含较多轮廓、纹理等细节信息,为更好地处理特征图中的高频分量与低频分量,实现自适应调整信道特征,提出一种基于特征图注意力机制的图像超分辨重建网络模型。利用特征提取块提取原始低分辨率图像中的特征信息,基于多个结合特征图注意力机制的信息提取块,通过特征信道之间的相互依赖性自适应调整信道特征,以恢复更多细节信息。在此基础上利用重建模块重建出不同尺度的高分辨率图像。在Set5数据集上的实验结果表明,与基于双三次插值的重建模型相比,该模型能够有效提升图像的视觉效果,且峰值信噪比与结构相似度分别提高了3.92 dB和0.056。

High-frequency components in image Super-Resolution(SR)reconstruction usually include more details such as contour and texture.In order to deal with the high-frequency components and low-frequency components in feature map better and adjust the channel features adaptively,this paper proposes an image SR reconstruction network model based on the attention mechanism.The model uses the feature extraction module to extract the feature information from the original Low-Resolution(LR)image.Then multiple information extraction modules using the attention mechanism of the feature map are used to adjust the channel features adaptively through the interdependence between the feature channels,so as to recover more detailed information.On this basis,the reconstruction module is used to reconstruct High-Resolution(HR)images of different scales.The experimental results on the Set5 dataset show that compared with the reconstruction model based on Bicubic interpolation,this model can effectively improve the visual effect of the image,and its Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity(SSIM)are improved by 3.92 dB and 0.056 respectively.