Project Overview
This picture is my final poster
Designed a survey, investigated the need to repair old videos or home movies, and clarified the technical development direction. Analyzed existing video frame interpolation technologies, simulated the similarity between adjacent frames using deep learning algorithms, predicted missing frames, combined traditional video interpolation algorithms with Generative Adversarial Networks (GANs), and incorporated diffusion model inference to reduce unnatural distortions.
Analyzed existing video frame interpolation technologies, simulated the similarity between adjacent frames using deep learning algorithms, predicted missing frames, combined traditional video interpolation algorithms with Generative Adversarial Networks (GANs), and incorporated diffusion model inference to reduce unnatural distortions.
Introduced diffusion inference blocks to enhance the semantic integrity of video frame generation, guided the network convergence using Lpips loss, performed ablation studies, and validated the effectiveness of each component.
Completed the algorithm design, wrote a report, won an award for the BJUT 2023 Spark Fund Excellent Project (24th Edition), and obtained a software copyright.
Achievement
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