基于多尺度特征融合反投影网络的图像超分辨率重建

针对现有图像超分辨率重建方法恢复图像高频细节能力较弱、特征利用率不足的问题, 提出了一种多尺度特征融合反投影网络用于图像超分辨率重建. 该网络首先在浅层特征提取层使用多尺度的卷积核提取不同维度的特征信息, 增强跨通道信息融合能力; 然后,构建多尺度反投影模块通过递归学习执行特征映射, 提升网络的早期重建能力; 最后,将局部残差反馈结合全局残差学习促进特征的传播和利用, 从而融合不同深度的特征信息进行图像重建. 对图像进行×2 ~ ×8超分辨率的实验结果表明, 本方法的重建图像质量在主观感受和客观评价指标上均优于现有图像超分辨率重建方法, 超分辨率倍数大时重建性能相比更优秀.

Multiscale Feature Fusion Back-projection Network for Image Super-resolution

Aiming at the problems that existing image super-resolution reconstruction methods have weak ability to restore image high-frequency details and insufficient feature utilization, a multi-scale feature fusion back projection network is proposed for image super-resolution reconstruction. The network first uses multi-scale convolution kernels in the shallow feature extraction layer to extract feature information of different dimensions to enhance cross-channel information fusion; then builds a multi-scale back projection module to perform feature mapping through recursive learning to improve the early reconstruction capabilities of the network; Finally, local residual feedback is combined with global residual learning to promote the spread and utilization of features, thereby fusing feature information of different depths for image reconstruction. The experimental results of ×2 ~ ×8 SR on the images show that the quality of SR image of this method is better than the existing image super-resolution method in subjective perception and objective evaluation index, and the reconstruction performance is relatively better when the scale factors is large.