Stochastic Deep Restoration Priors for Imaging Inverse Problems
Authors: Yuyang Hu, Albert Peng, Weijie Gan, Peyman Milanfar, Mauricio Delbracio, Ulugbek S. Kamilov
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We numerically validate Sha RP on two inverse problems of the form y = Ax+e: (Compressive Sensing MRI (CS-MRI) and (b) Single Image Super Resolution (SISR). In both cases, e represents additive white Gaussian noise (AWGN). For the data-fidelity term in eq. (2), we use the ℓ2-norm loss for both problems. Quantitative performance is evaluated by Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). |
| Researcher Affiliation | Collaboration | Yuyang Hu 1 Albert Peng 1 Weijie Gan 1 Peyman Milanfar 2 Mauricio Delbracio 2 Ulugbek S. Kamilov 1 1Wash U 2Google. Correspondence to: Ulugbek S. Kamilov <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Stochastic deep Restoration Priors (Sha RP) Algorithm 2 Supervised Training of CS-MRI Restoration Network Algorithm 3 Self-Supervised Training of CS-MRI Restoration Network Algorithm 4 Gaussian Deblurring Restoration network training Algorithm 5 MRI Super Resolution network training |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It references third-party codebases used for training or test data, such as "official implementation of DDS2" (https://github.com/HJ-harry/DDS) and "I2SB" (https://github.com/NVlabs/I2SB), and a testset from Diff PIR (https://github.com/yuanzhi-zhu/DiffPIR/tree/main/testsets), but no specific link or statement about the open-sourcing of Sha RP's implementation. |
| Open Datasets | Yes | We utilized the open-access fast MRI dataset; further experimental details can be found in Section B.1 of the supplementary material. We randomly selected 100 images from the Image Net test set, as provided in Diff PIR1. |
| Dataset Splits | Yes | We simulated multi-coil subsampled measurements using T2-weighted human brain MRI data from the open-access fast MRI dataset, which comprises 4,912 fully sampled multi-coil slices for training and 470 slices for testing. Each slice has been cropped into a complex-valued image with dimensions 320 × 320. To ensure fairness, for each problem setting, each method both proposed and baseline is fine-tuned for optimal PSNR using 10 slices from a validation set separate from the test set. The same step size γ and regularization parameter τ are then applied consistently across the entire test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like "Adam optimizer" and "U-Net architecture" but does not specify any version numbers for programming languages, libraries, or frameworks used (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | The model is trained with Adam optimizer with a learning rate of 5e-5. We select 1,000 different α values to train the model, following the α schedule outlined by I2SB (Liu et al., 2023). |