Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.10014 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918148357226496 |
|---|---|
| author | Wang, Xingjian Chai, Li Chen, Jiming |
| author_facet | Wang, Xingjian Chai, Li Chen, Jiming |
| contents | The performance of single image super-resolution depends heavily on how to generate and complement high-frequency details to low-resolution images. Recently, diffusion-based DDPM models exhibit great potential in generating high-quality details for super-resolution tasks. They tend to directly predict high-frequency information of wide bandwidth by solely utilizing the high-resolution ground truth as the target for all sampling timesteps. However, as a result, they encounter hallucination problem that they generate mismatching artifacts. To tackle this problem and achieve higher-quality super-resolution, we propose a novel Frequency Domain-guided multiscale Diffusion model (FDDiff), which decomposes the high-frequency information complementing process into finer-grained steps. In particular, a wavelet packet-based frequency degradation pyramid is developed to provide multiscale intermediate targets with increasing bandwidth. Based on these targets, FDDiff guides reverse diffusion process to progressively complement missing high-frequency details over timesteps. Moreover, a multiscale frequency refinement network is designed to predict the required high-frequency components at multiple scales within one unified network. Comprehensive evaluations on popular benchmarks are conducted, and demonstrate that FDDiff outperforms prior generative methods with higher-fidelity super-resolution results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10014 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution Wang, Xingjian Chai, Li Chen, Jiming Computer Vision and Pattern Recognition Image and Video Processing The performance of single image super-resolution depends heavily on how to generate and complement high-frequency details to low-resolution images. Recently, diffusion-based DDPM models exhibit great potential in generating high-quality details for super-resolution tasks. They tend to directly predict high-frequency information of wide bandwidth by solely utilizing the high-resolution ground truth as the target for all sampling timesteps. However, as a result, they encounter hallucination problem that they generate mismatching artifacts. To tackle this problem and achieve higher-quality super-resolution, we propose a novel Frequency Domain-guided multiscale Diffusion model (FDDiff), which decomposes the high-frequency information complementing process into finer-grained steps. In particular, a wavelet packet-based frequency degradation pyramid is developed to provide multiscale intermediate targets with increasing bandwidth. Based on these targets, FDDiff guides reverse diffusion process to progressively complement missing high-frequency details over timesteps. Moreover, a multiscale frequency refinement network is designed to predict the required high-frequency components at multiple scales within one unified network. Comprehensive evaluations on popular benchmarks are conducted, and demonstrate that FDDiff outperforms prior generative methods with higher-fidelity super-resolution results. |
| title | Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2405.10014 |