Improving Generative Adversarial Networks for Video Super-Resolution
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910499689463808 |
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| author | Wen, Daniel |
| author_facet | Wen, Daniel |
| contents | In this research, we explore different ways to improve generative adversarial networks for video super-resolution tasks from a base single image super-resolution GAN model. Our primary objective is to identify potential techniques that enhance these models and to analyze which of these techniques yield the most significant improvements. We evaluate our results using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Our findings indicate that the most effective techniques include temporal smoothing, long short-term memory (LSTM) layers, and a temporal loss function. The integration of these methods results in an 11.97% improvement in PSNR and an 8% improvement in SSIM compared to the baseline video super-resolution generative adversarial network (GAN) model. This substantial improvement suggests potential further applications to enhance current state-of-the-art models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_16359 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Generative Adversarial Networks for Video Super-Resolution Wen, Daniel Image and Video Processing Computer Vision and Pattern Recognition F.2.2; I.2.7 In this research, we explore different ways to improve generative adversarial networks for video super-resolution tasks from a base single image super-resolution GAN model. Our primary objective is to identify potential techniques that enhance these models and to analyze which of these techniques yield the most significant improvements. We evaluate our results using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Our findings indicate that the most effective techniques include temporal smoothing, long short-term memory (LSTM) layers, and a temporal loss function. The integration of these methods results in an 11.97% improvement in PSNR and an 8% improvement in SSIM compared to the baseline video super-resolution generative adversarial network (GAN) model. This substantial improvement suggests potential further applications to enhance current state-of-the-art models. |
| title | Improving Generative Adversarial Networks for Video Super-Resolution |
| topic | Image and Video Processing Computer Vision and Pattern Recognition F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2406.16359 |