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..
QFormer: An Efficient Quaternion Transformer for Image Denoising
Authors: Bo Jiang, Yao Lu, Guangming Lu, Bob Zhang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate that the proposed QFormer produces state-of-the-art results in both denoising performance and efficiency. |
| Researcher Affiliation | Academia | Bo Jiang1 , Yao Lu2 , Guangming Lu2 and Bob Zhang3 1College of Mechanical and Electronic Engineering, Northwest A&F University, China 2Department of Computer Science, Harbin Institute of Technology at Shenzhen, China 3Department of Computer and Information Science, University of Macau, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, such as a specific repository link or explicit code release statement. |
| Open Datasets | Yes | Table 1: Average PSNR and SSIM of the denoised real images from Nam, Poly U and SIDD datasets. |
| Dataset Splits | No | The paper mentions using Nam, Poly U, and SIDD datasets but does not provide specific details on train/validation/test splits, exact percentages, or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models or processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions architectural parameters like feature channels (C=16, 32, 44) and QTB depth (N=2), but does not provide specific experimental setup details such as learning rates, batch sizes, or optimizer settings. |