The existing deep learning-based image blind super-resolution algorithms only utilize neural networks to learn the end-to-end mapping from low-resolution (LR) images to high-resolution (HR) images, only allowing the network to implicitly learn image priors, resulting in algorithms that still produce blurry super-resolution results. To address the above issues, a deep learning image blind super-resolution algorithm guided by sparsity and self-similarity priors is proposed. Initially, for various LR image inputs, a dynamic linear kernel estimation module is employed to effectively estimate the corresponding blur kernels; Subsequently, a deep unfolding deconvolution filtering module based on the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) is utilized to explicitly model the sparsity prior of signal, achieving deconvolution restoration of the degraded images; Finally, a dual-path multi-scale large receptive field restoration module leverages the self-similarity prior of images for super-resolution recovery. The experimental results indicate that, compared to existing methods, the proposed algorithm achieves a peak signal-to-noise ratio (PSNR) of 31.66 and a structural similarity index (SSIM) of 0.8725 on the publicly available Gaussian8 dataset, and attains a PSNR of 29.08 and an SSIM of 0.8007 on the DIV2KRK dataset. The images restored by the proposed algorithm not only exhibit the highest restoration metrics but also superior visual quality.