Image deblurring has attracted much attention due to its wide applications in fields such as security surveillance, medical image processing, and remote sensing image processing. Although end-to-end methods have made significant progress, a single U-Net network struggles to handle complex motion blur, while restoration approaches based on auxiliary tasks often suffer from large parameter sizes. In addition, the vast majority of methods fail to accurately identify the locations and degrees of blur in different images, while blur perception is often one of the key factors determining the restoration performance of models. Inspired by this, this study proposes a progressive image deblurring algorithm guided by blur perception (PDBP-Net). The main idea of the algorithm is to utilize auxiliary tasks to generate blur perception feature maps, thus guiding the algorithm to achieve more refined restoration. First, the high-frequency difference and image residual generative subnetwork (HDIRG-net) employs auxiliary learning to simultaneously generate high-frequency difference feature maps and residual maps. These are then fed into the blur perception module guided by high-frequency differences (BPGHD) for deep fusion and extraction of blur-related information, resulting in the generation of blur perception feature maps. Moreover, to alleviate the limitations of a single network in restoring complex scenes, this module uses the residual maps and blur maps to generate preliminary restored images. Finally, the blur perception-guided detail restoration subnetwork (BPGDR-net) conducts targeted re-optimization of the preliminary restored images under the guidance of the blur perception feature maps, thus generating the final restored images. The proposed deblurring model is extensively evaluated on multiple benchmark datasets and achieves significant improvements over state-of-the-art deblurring methods. Specifically, on the GoPro dataset, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) reach 33.85 dB and 0.967, respectively, with the PSNR being 0.39 dB higher than that of the second-best method. Extensive experiments demonstrate that PDBP-Net outperforms state-of-the-art auxiliary learning-based methods and significantly enhances image deblurringperformance, confirming the effectiveness of the proposed method.