Research on an image color restoration method for old art films

The preservation and restoration of old art films have certain practical value. Focusing on the color restoration of old art films, this paper introduced the same mapping loss on the basis of a cycle-consistent generative adversarial network, which enables the algorithm to capture more image details and achieve better transfer results. The old art films Roman Holiday and Tracks in the Snowy Forest were used as experimental data to verify the color restoration effect of the improved cycle-consistent generative adversarial network algorithm. It was found that compared with the generative adversarial network and cycle-consistent generative adversarial network algorithms, the improved cycle-consistent generative adversarial network algorithm was superior. It achieved a peak signal-to-noise ratio of 26.874, a structural similarity index measure of 0.665, a learned perceptual image patch similarity of 0.212, and a Frechet inception distance of 117.652 for Roman Holiday. Moreover, it achieved a peak signal-to-noise ratio of 22.794, a structural similarity index measure of 0.585, a learned perceptual image patch similarity of 0.247, and a Frechet inception distance of 119.265 for Tracks in the Snowy Forest. It also achieved better results in comparison with existing image color restoration methods. The results demonstrate the usability of the improved cycle-consistent generative adversarial network algorithm in color restoration of old art films, which can be applied in practice.