Enhancing the Restoration of Old Damaged Reel Images Using a Novel Long Short-Term Memory Algorithm Compared to Generative Adversarial Networks for Improved Accuracy

The aim of this study is to enhance the regeneration of old, damaged film images using a novel LSTM algorithm, comparing its performance to that of GANs for greater accuracy in a sustainable environment. We analyzed 20 accuracy samples from a Kaggle dataset, divided into two groups of 10 samples each, to measure performance improvements in image regeneration. A statistical power of 0.8 was used for analysis. Results show that the novel LSTM achieved an accuracy of 88.99 %, while the GAN recorded 82.18 %. The two-tailed significance value was 0.024 ($p<0.05$), indicating that this result is statistically significant. In summary, these results are evidence that the new LSTM outperformed GAN in the regeneration of old, damaged film images with regard to their accuracy.