Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
EvFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Authors: Lin Zhu, Xiantao Ma, Xiao Wang, Lizhi Wang, Hua Huang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both simulated and real-world datasets demonstrate that Ev Focus outperforms existing methods across varying lighting conditions and blur sizes, proving its robustness and practical applicability in event-based defocus imaging. |
| Researcher Affiliation | Academia | 1School of Computer Science& Technology, Beijing Institute of Technology, Beijing, China 2School of Computer Science, Anhui University, Hefei, China 3School of Artificial Intelligence, Beijing Normal University, Beijing, China. Correspondence to: Hua Huang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Synthetic Data Generation Pipeline Require: A set of background images {Bi} A set of foreground images {Fi,j} for each background i Motion parameters (e.g. translation, rotation) for background & foreground Number of time steps T Ensure: Synthetic dataset containing rendered scenes with events & optical flow 1: for each scene i do 2: Select one background image Bi 3: Select M foreground images {Fi,j}M j=1 4: Generate motion trajectories for background and each foreground: traj B Generate Trajectory(motion parameters) traj Fi,j Generate Trajectory(motion parameters) 5: for t = 1, . . . , T do 6: Sample camera pose pt Sample Pose() 7: Sample camera distortion dt Sample Distortion() 8: Render defocus brightness image: It Render(Bi, {Fi,j}, traj B[t], {traj Fi,j[t]}, pt, dt) 9: Compute brightness change It = It It 1 (if t > 1) 10: Generate events Et Event Generation( It) 11: Compute optical flow ut Optical Flow(It) 12: end for 13: Store {It}T t=1, {Et}T t=1, {ut}T t=1 as the dataset for scene i 14: end for |
| Open Source Code | No | The text does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | As stated in Sec. 2, we generate sequences of defocus events, sharp images, and optical flows, in which 41 sequences are used in the training set and 6 sequences in the test set. To verify the effectiveness of our model on real data, we use the DAVIS 346 cameras to capture 7 real-world scenes. |
| Dataset Splits | Yes | As stated in Sec. 2, we generate sequences of defocus events, sharp images, and optical flows, in which 41 sequences are used in the training set and 6 sequences in the test set. |
| Hardware Specification | Yes | Our model is trained for 300 epochs with batch size of 1 on 3 NVIDIA Ge Force RTX 3090 GPUs. |
| Software Dependencies | No | Our model is implemented using the Py Torch framework. The paper mentions a software framework (PyTorch) but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | Yes | We adopt a constant strategy of learning rate during training, which is set at 1e-4. Our model is trained for 300 epochs with batch size of 1 on 3 NVIDIA Ge Force RTX 3090 GPUs. |