To address the challenges in traditional image restoration algorithms based on regularization models that the information encapsulated in the regularization term prior may not be sufficiently rich, and determining the regularization coefficients can be cumbersome or require adaptive adjustments, combining the advantages of traditional and deep learning methods, this paper combines L2-norm regularization with deep learning, proposes a deep learning network with strict mathematical model foundation and interpretability: interpretable deep learning image restoration algorithm with L2-norm prior. Nonlinear transformations are employed to replace the regularization term in the traditional model, and deep learning networks are utilized to solve the regularization model. This not only optimizes the model solving process but also enhances the interpretability of the deep learning network. Experimental results demonstrate that the proposed algorithm is capable of effectively removing image blurriness while suppressing image noise, thereby improving image quality.