Help me retouch the following paragraphs with academic styleIndeed due to our negligence in the increased comparison we intended to demonstrate the robustness of the proposed model by comparing its pr

In order to demonstrate the robustness of the proposed model, a comparison was conducted with the DCP method, which yielded the best processing results for background motion. However, the proposed algorithm only won 2 out of 6 videos, while the deep learning algorithm won 4, indicating that the superiority of the proposed algorithm over the deep learning algorithm was not reflected. It should be noted, however, that the proposed algorithm is primarily designed to address the background construction problem in intermittent motion videos, and in this type of video processing, it has distinct advantages.

In the revised version, a background comparison of the reconstructed intermittent motion videos with the DCP algorithm was added based on the data provided in the comparative paper. The comparison results showed that the proposed algorithm won 15 out of 16 videos, with only 1 video showing a weaker indicator than the DCP algorithm. The competition result was 15:1. Additionally, a comparison with the five videos provided by the BI-GAN algorithm was included, which evaluated AGe, pEPs, pCEPS, MSSSiM, and PSNR. The results showed that BI-GAN only won the pEPs metric and pCEPS metric in the HallAndMonitor video. Overall, the proposed model won 28 out of 6 video comparison data, and the result of the competition was 28:2.

These findings suggest that the proposed model has certain advantages and robustness over deep learning algorithms for intermittent videos, as demonstrated by the metrics of the newly added DCP algorithm for processing intermittent motion videos, as well as the metrics of the BI-GAN algorithm for the SBI dataset