Region-Adaptive Dense Network for Efficient Motion Deblurring
Authors: Kuldeep Purohit, A. N. Rajagopalan11882-11889
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive comparisons with prior art on benchmark dynamic scene deblurring datasets clearly demonstrate the superiority of the proposed networks via significant improvements in accuracy and speed, enabling almost real-time deblurring. |
| Researcher Affiliation | Academia | Kuldeep Purohit, A. N. Rajagopalan Indian Institute of Technology Madras, India kuldeeppurohit3@gmail.com, raju@ee.iitm.ac.in |
| Pseudocode | No | The paper provides architectural diagrams and descriptions but does not include pseudocode or explicit algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | we perform training and evaluation of our network on the dynamic scene deblurring benchmark (Nah, Kim, and Lee 2017) |
| Dataset Splits | Yes | Following the same train-test split as in (Nah, Kim, and Lee 2017), we use 2103 pairs for training and 1111 pairs for evaluation. |
| Hardware Specification | Yes | We conduct our experiments on a PC with Intel Xeon E5 CPU, 256 GB RAM and an NVIDIA Titan X GPU. |
| Software Dependencies | No | The paper mentions optimizers and frameworks indirectly through citations, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Training is done for 1 × 10^6 iterations using Adam optimizer with learning rate 0.0001 on patches of 256 × 256 and batch-size of 16. |