基于特征融合和注意力机制的图像超分辨率模型

现有的基于深度学习的单张图像超分辨率(single image super-resolution, SISR)模型通常是通过加深网络层数来提升模型的拟合能力,没有充分提取和复用特征,导致重建图像的质量较低。针对该问题,提出了基于特征融合和注意力机制的图像超分辨率模型。该模型在特征提取模块使用残差中嵌入残差(residual in residual, RIR)的结构,该网络的特征提取模块由包含多个残差块的残差组构成,并且在每个残差组内进行局部特征融合,在每个组之间进行全局特征融合。此外,在每一个残差块中引入坐标注意力模块,在每一个残差组中引入空间注意力模块。经验证,该模型能充分提取特征并且复用特征。实验最终结果表明,该模型在客观评价指标和主观视觉效果上都优于现有的模型。

Image super-resolution model based on feature fusion and attention mechanism

Existing deep learning based single image super-resolution(SISR) models usually improve the fitting ability of the model by increasing the number of network layers, but fail to fully extract and reuse features, leading low quality of reconstructed images.To solve this problem, this paper proposed an image super-resolution model based on feature fusion and attention mechanism.This model used residual in residual(RIR) structure in feature extraction module.The feature extraction module of the network consisted of several residual groups.Each residual group consisted of several residual block.This module implemented local feature fusion in each residual group and global feature fusion between each group.In addition, this model introduced coordinate attention module into each residual block and spatial attention module into each residual group.It verifies that the model is able to fully extract features and reuse features.The final experimental results show that the model is superior to the existing models in objective evaluation indexes and subjective visual effect.