多尺度密集特征融合的图像超分辨率重建

Multi-scale dense feature fusion network for image super-resolution

针对现有单幅图像超分辨率重建算法提取的图像特征信息单一、高频细节丢失的问题,提出了一种高效利用特征信息的基于多尺度密集特征融合网络的图像超分辨率重建算法。该方法通过含有不同尺度卷积核的多尺度特征融合残差模块提取不同尺度图像特征并将不同尺度的特征融合,以提取丰富的图像特征。在模块间采用密集特征融合结构将不同模块提取到的特征信息充分融合,以更好地保留图像的高频细节、获取更好的视觉感受。大量实验表明,所提出的方法在参数量减少的同时,在四个基准数据集上取得的峰值信噪比和结构相似度均有明显提升,尤其在Set5数据集上4倍重建结果的峰值信噪比相比于DID-D5提升了0.08 dB,且重建图像视觉效果更好、特征信息更加丰富,充分证明了该算法的有效性。

Existing single-image super-resolution algorithms lose high-frequency details and cannot extract rich image features.Therefore,an image super-resolution reconstruction algorithm based on a multi-scale dense feature fusion network is proposed to efficiently utilize image features.This algorithm extracts image features of different scales by employing the multi-scale feature fusion residual module with convolution kernels of different scales.It fuses different scale features to better preserve the high-frequency details of images.A dense feature fusion structure is adopted between modules to fully integrate the feature information extracted from different modules,to avoid feature information loss and obtain better visual feeling.Several experiments show that the proposed method can significantly improve the peak signal-to-noise ratio and structural similarity on four benchmark datasets while reducing the number of parameters.In particular,on the Set5 dataset,compared with DID-D5,the peak signal-to-noise ratio of 4×super-resolution increases by 0.08 dB and the reconstructed image has better visual effects and richer feature information,thus confirming the effectiveness of the proposed algorithm.