Application of image restoration technology based on deep learning in intelligent monitoring system

Image restoration techniques aim to recover the original appearance of degraded images through mathematical models and algorithms, involving the analysis and compensation for causes of image degradation. This paper introduces various image restoration techniques, including spatial domain filtering, frequency domain filtering, inverse filtering, Wiener filtering, among others, and discusses their advantages and disadvantages. Furthermore, it elaborates on the critical implementations of deep learning-based image restoration techniques, focusing on network architecture design and loss function design. Through experimental analysis, this paper verifies the effectiveness of deep learning methods in image restoration tasks. Compared with traditional methods, deep learning approaches exhibit significant superiority in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Lastly, the paper addresses the limitations of deep learning models in image restoration and suggests potential improvement directions, such as optimizing network architectures, refining loss function designs, enhancing data diversity, and exploring lightweight model designs. These research findings not only enhance the performance and reliability of intelligent surveillance systems but also provide new ideas and methods for the development of image restoration technology.