Blind image super-resolution reconstruction focuses on recovering Low-Resolution （LR） images into clear High-Resolution （HR） images in real-world scenarios. Most existing degradation estimation networks cannot accurately estimate the degradation information of LR images， and the traditional Residual-in-Residual Dense Blocks （RRDBs） in super-resolution networks have poor generalization ability， which fail to adapt to complex degradation types. To address the above issues， a blind image Super-Resolution reconstruction method by Integrating Degradation estimation and Dynamic residual-in-residual dense blocks （IDD-SR） was proposed. First， an integrating degradation estimation network was proposed and used for accurate estimation of image degradation information. Then， a Dynamic Residual-in-Residual Dense Block （DRRDB） was proposed to improve the universality of the super-resolution reconstruction network. Finally， accurate prediction of complex degradation information and high-quality super-resolution image reconstruction were achieved through end-to-end joint optimization. Experimental results on the Set14， BSD100， and DRealSR datasets demonstrate that the Peak Signal-to-Noise Ratio （PSNR） values of 4× super-resolution of IDD-SR are improved by 0.90 dB， 0.71 dB， and 0.97 dB， respectively， compared with those of PDM-SRGAN. It can be seen that the proposed method has superior detail recovery capability and artifact suppression effect.