Blind Image Restoration via Fast Diffusion Inversion
Authors: Hamadi Chihaoui, Abdelhak Lemkhenter, Paolo Favaro
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate BIRD on several image restoration tasks and show that it achieves state of the art performance. Project page: https://hamadichihaoui.github.io/BIRD. |
| Researcher Affiliation | Academia | Hamadi Chihaoui Abdelhak Lemkhenter Paolo Favaro Computer Vision Group, Institute of Informatics, University of Bern, Switzerland {hamadi.chihaoui,abdelhak.lemkhenter,paolo.favaro}@unibe.ch |
| Pseudocode | Yes | Algorithm 1 BIRD: Image Restoration; Algorithm 2 DDIMReverse ( x T , δt) |
| Open Source Code | Yes | Project page: https://hamadichihaoui.github.io/BIRD. We include the training details and we provide the code in the Supplementary. |
| Open Datasets | Yes | We evaluate our method both on the validation datasets of Image Net [5] and Celeb A [10] at 256 256 pixel resolution. |
| Dataset Splits | Yes | We evaluate our method both on the validation datasets of Image Net [5] and Celeb A [10] at 256 256 pixel resolution. The PSNR mean and standard deviation are computed over 10 runs. |
| Hardware Specification | Yes | All experiments are carried out on a Ge Force GTX 1080 Ti. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For all our experiments, we use δt = 100 and a maximum number of iterations N = 200. Adam is adopted as an optimizer with a 0.003 learning rate. |