Visual Art Image Restoration Based on Regularized Low-rank Matrix Restoration Algorithm

In the digital age, visual art images serve as important carriers of information transmission and aesthetic expression, and its integrity and quality are crucial. To repair damaged or degraded art images, a regularized low-rank matrix restoration algorithm is designed to repair visual art images. A low-rank matrix recovery method based on regularized singular values is proposed by incorporating regularization strategies and singular value entropy functions. This algorithm repairs visual art images of different types and styles, and evaluates its restoration effects. From the experimental results, the relative error of the low-rank matrix restoration algorithm based on regularized singular value function was 0.001, the running time was 28.54 seconds, and the F1 value was 92.51. The algorithm had a relatively high peak signal-to-noise ratio on different images, with an average of 0.93. The results indicate that the low-rank matrix restoration algorithm based on regularized singular value function has good image quality and small difference from the original image. The regularized low-rank matrix restoration algorithm can effectively repair visual art images and improve image quality and observability. The research provides solid theoretical support for image restoration, presents strong guidance for algorithm design and improvement, and displays useful reference and guidance for other related fields.