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..
Masked Pre-training Enables Universal Zero-shot Denoiser
Authors: Xiaoxiao Ma, Zhixiang Wei, Yi Jin, Pengyang Ling, Tianle Liu, Ben Wang, Junkang Dai, Huaian Chen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments across various noisy scenarios underscore the notable advances of MPI over previous approaches with a marked reduction in inference time. Code available at https://github.com/krennic999/MPI. and 3 Experiments We assess our method against typical methods including DIP [13], Noise2Void (N2V) [16], Noise2Self (N2S) [17], Zero-Shot Noise2Noise (ZS-N2N) [22], and Faster DIP [19]. |
| Researcher Affiliation | Academia | Xiaoxiao Ma1 Zhixiang Wei1 Yi Jin1 Pengyang Ling1,2 Tianle Liu1 Ben Wang1 Junkang Dai1 Huaian Chen1 1 University of Science and Technology of China 2 Shanghai AI Laboratory EMAIL {jinyi08}@ustc.edu.cn |
| Pseudocode | Yes | Algorithm 1: Iterative filling. Pipeline designed to leverage pre-trained representation θ for zero-shot denoising. Input: Noisy image x, pre-trained parameter θ, network D( ), exponential weight β, masking ratio p. Output: denoised ensemble y from predictions of iteration {yt}. |
| Open Source Code | Yes | Code available at https://github.com/krennic999/MPI. |
| Open Datasets | Yes | Pre-training. Pre-training is performed on two Nvidia RTX 3090 GPUs using Adam optimizer with β1=0.9 and β2=0.9. Initial learning rate is 2e 3 and decays to 1e 5 with cosine annealing strategy over 80K iterations with a batch size of 64. We initiate pre-training on randomly cropped 256 256 patches from subset of Image Net [28] with around 48,000 images. We investigate Gaussian Noise with σ [10,25,50] and Poisson noise with λ [10, 25, 50] separately on three datasets: CSet [3], Mc Master [33] and CBSD [34], with 9, 18 and 68 high-quality images, respectively. |
| Dataset Splits | Yes | We test on SIDD [1] and Poly U [45] datasets, including 1280 patches from the SIDD validation and 1280 from SIDD benchmark, and all 100 official patches from Poly U to show our paradigm on real images. |
| Hardware Specification | Yes | Pre-training. Pre-training is performed on two Nvidia RTX 3090 GPUs using Adam optimizer with β1=0.9 and β2=0.9. |
| Software Dependencies | No | The paper mentions ‘Adam optimizer’ and ‘U-shaped hourglass architecture as in DIP [13]’ but does not provide specific version numbers for any software, libraries, or frameworks used for implementation or experimentation. |
| Experiment Setup | Yes | Pre-training. Pre-training is performed on two Nvidia RTX 3090 GPUs using Adam optimizer with β1=0.9 and β2=0.9. Initial learning rate is 2e 3 and decays to 1e 5 with cosine annealing strategy over 80K iterations with a batch size of 64. ... Zero-shot inference. We set learning rate during inference to 2e 3, and same masking ratio p as pre-training (0.3 for synthetic, 0.8 0.95 for real noise) is set. EMA weight β=0.99 for 1000 iterations (specially, 800 iterations for SIDD). Additionally, with β=0.9, we achieve performance surpassing most zero-shot methods within 200 iterations, denoted as faster . |