Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Generative Adaptive Convolutions for Real-World Noisy Image Denoising
Authors: Ruijun Ma, Shuyi Li, Bob Zhang, Zhengming Li1935-1943
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,086 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,226 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the superior denoising performances of the proposed FADNet versus the state-of-the-art. In contrast to the existing deep denoisers, our FADNet is not only flexible and efficient, but also exhibits a compelling generalization capability, enjoying tremendous potential for practical usage. |
| Researcher Affiliation | Academia | Ruijun Ma1, 2, Shuyi Li1, Bob Zhang1 , Zhengming Li2 1 PAMI Research Group, Department of Computer and Information Science, University of Macau 2 Guangdong Industrial Training Center, Guangdong Polytechnic Normal University |
| Pseudocode | No | The paper describes the network architecture and method steps but does not include formal pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement about open-source code availability or a link to a code repository. |
| Open Datasets | Yes | In this work, the training data was from SIDD (Abdelhamed, Lin, and Brown 2018). |
| Dataset Splits | Yes | SIDD also provided a medium version package, in which 320 images pairs were leveraged for fast training and 1280 images pairs for validation purposes. |
| Hardware Specification | Yes | All the experiments were carried out using the Pytorch library (Paszke et al. 2019) on a machine with an NVIDIA Titan Xp GPU. |
| Software Dependencies | No | The paper mentions PyTorch and Adam optimizer but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We utilized the Adam optimizer (Kingma and Ba 2014) to update the network, with β1 = 0.7, β2 = 0.999, and ϵ = 10 8. The learning rate was initially set as 0.001 and reduced to 0.0001 when the training errors held steady. |