基于退化感知和序列残差的图像盲超分辨率重建

Blind image super-resolution reconstruction based on degradation aware and sequence residuals

针对盲超分辨率重建中特征提取不准确且重建图像不够自然的问题,提出了一种基于退化感知和序列残差的图像盲超分辨率重建算法,设计了小残差组融合退化感知和序列残差相结合作为所提算法的主干网络,进一步构建了对称的增强型多尺度残差模块,并且在图像重建部分,将瓶颈注意力模块与像素重组上采样模块级联,强调图像的多维元素,最后进行了全局残差连接。实验表明,与当前代表性算法DASR相比,该算法在Set14×2上的PSNR和SSIM分别提高0.145 dB、0.0014,在Set14×3/4上PSNR分别提高1.898 dB、0.252 dB,且在五个标准测试集上与几种当前流行的图像超分辨率算法相比取得了更好的性能。

Aiming at the problem that feature extraction is inaccurate and the reconstruction image is not natural enough in blind super-resolution reconstruction,this paper proposed an image blind super-resolution reconstruction based on degradation aware and sequence residuals.This paper proposed a mini-residual group combined degeneration aware and sequence residuals as the backbone network.Then the method constructed a symmetrical enhanced multi-scale residual block.In the image reconstruction part,this paper used the bottleneck attention module and the sub-pixel convolutional module to emphasize the multi-dimensional elements of the image.Finally,the method made a global residual connection.Compared with the current representative algorithm DASR,experiments show that the PSNR and SSIM of the proposed algorithm are improved 0.145 dB and 0.0014 on Set14×2,and the PSNR of the proposed algorithm is improved 1.898 dB and 0.252 dB on Set14×3/4,respectively.The proposed algorithm achieves better performance than several current image super-resolution algorithms on five standard test sets.