Single-image super-resolution (SISR) reconstruction has highly academic and practical values. The deep plug-and-play super-resolution (DPSR) framework has been proposed to super-resolve low-resolution (LR) images with arbitrary blur kernels. However, DPSR does not make full use of hierarchical features from original LR images, thereby achieving relatively-low performance, such as getting low average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values. Considering residual-in-residual dense block (RRDB) can exploit hierarchical features, in this paper, firstly, RRDB is introduced to design an improved DPSR (IDPSR) framework with RRDB for arbitrary blur kernels. Secondly, the RRDB is adopted to replace the deep feature extraction part in DPSR in order to extract abundant local features, which makes the network capacity higher benefiting from the dense connections. The residual learning in different levels in RRDB can obtain high quality images. Finally, the test experiments are based on Set5, Set14, Urban100 and BSD100 datasets. The experimental results show that, under different blur kernels and different scale factors, PSNR and SSIM values of our proposed method increase by 0.34dB and 0.68%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.27dB and 1.01%, respectively.

单图像超分辨率（SISR）重建具有很高的学术和实用价值。深度即插即用超分辨率（DPSR）框架已被提出来超分辨率具有任意模糊内核的低分辨率（LR）图像。然而，DPSR没有充分利用原始LR图像的层次特征，从而实现了相对较低的性能，例如获得较低的平均峰值信噪比（PSNR）和结构相似性（SSIM）值。考虑到残差密集块（RRDB）可以利用层次特征，本文首先引入RRDB，针对任意模糊核设计一种改进的DPSR（IDPSR）框架。其次，采用RRDB代替DPSR中的深度特征提取部分，以提取丰富的局部特征，得益于密集的连接，网络容量更高。RRDB中不同层次的残差学习可以获得高质量的图像。最后，测试实验基于Set5、Set14、Urban100和BSD100数据集。实验结果表明，在不同模糊核和不同尺度因子下，我们提出的方法的PSNR和SSIM值分别增加了0.34dB和0.68%；在不同噪声水平下，平均PSNR和SSIM值分别增加0.27dB和1.01%。我们提出的方法的PSNR和SSIM值分别增加了0.34dB和0.68%；在不同噪声水平下，平均PSNR和SSIM值分别增加0.27dB和1.01%。我们提出的方法的PSNR和SSIM值分别增加了0.34dB和0.68%；在不同噪声水平下，平均PSNR和SSIM值分别增加0.27dB和1.01%。