Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution
Authors: Zihang Liu, Zhenyu Zhang, Hao Tang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both real-world and synthetic datasets demonstrate that SAMSR significantly improves perceptual quality and detail recovery, particularly in semantically complex images. |
| Researcher Affiliation | Academia | 1Beijing Institute of Technology 2Nanjing University 3School of Computer Science, Peking University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Training the Pixel-wise Sampling Framework |
| Open Source Code | Yes | Our code is released at https://github.com/Liu-Zihang/SAMSR. |
| Open Datasets | Yes | For real-world evaluation, we utilize Real SR [Cai et al., 2019b] and Real Set65 [Yue et al., 2024]. For synthetic datasets, we follow the standard pipeline to create LR inputs from 3000 HR images randomly selected from Image Net [Wang et al., 2024]. |
| Dataset Splits | No | For synthetic datasets, we follow the standard pipeline to create LR inputs from 3000 HR images randomly selected from Image Net [Wang et al., 2024]. The paper does not explicitly provide details on how these images were split into training, validation, or test sets for their experiments. |
| Hardware Specification | Yes | Table 5: A comparison of the training time cost and results on NVIDIA RTX4090. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers. |
| Experiment Setup | Yes | Specifically, our model achieves convergence in only 10,000-15,000 iterations. The hyper-parameter m controls the noise addition speed and intensity for pixels with different levels of semantic richness during the forward diffusion process. ... Therefore, in this paper, we set m to 1/5. ... where ΜΈ is a hyper-parameter that controls the contribution of the semantic consistency loss. |