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 [1].
Model-Based Image Signal Processors via Learnable Dictionaries
Authors: Marcos V. Conde, Steven McDonagh, Matteo Maggioni, Ales Leonardis, Eduardo Pérez-Pellitero481-489
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evidence the value of our data generation process by extensive experiments under both RAW image reconstruction and RAW image denoising tasks, obtaining state-of-the-art performance in both. |
| Researcher Affiliation | Collaboration | Huawei Noah s Ark Lab *Marcos V. Conde is now with Computer Vision Lab, Institute of Computer Science, University of W urzburg. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the public availability of its source code in the main text. |
| Open Datasets | Yes | SIDD (Abdelhamed, Lin, and Brown 2018; Abdelhamed, Timofte, and Brown 2019). ... MIT-Adobe Five K dataset (Bychkovsky et al. 2011). We use the train-test sets proposed by Inv ISP (Xing, Qian, and Chen 2021) for the Canon EOS 5D and the Nikon D700, and the same processing using the Lib Raw library to render ground-truth s RGB images from the RAW images. |
| Dataset Splits | Yes | There are 320 ultra-high-resolution image pairs available for training (e.g. 5328 3000). Validation set consist of 1280 image pairs. |
| Hardware Specification | No | The paper states 'For more details we refer the reader to the supplementary material, where we also provide other relevant information about the training process e.g. GPU devices', but it does not provide specific hardware models (like GPU types, CPU types, or memory) in the main text. |
| Software Dependencies | No | The paper mentions 'Lib Raw library' for processing, but it does not specify versions for software dependencies or other key libraries used in the experiments. |
| Experiment Setup | No | The paper mentions 'batch sizes, network architectures' are provided in the supplementary material, but it does not provide specific hyperparameter values or detailed training configurations in the main text. |