Analyzing the Sample Complexity of Self-Supervised Image Reconstruction

Each folder contains the code to reproduce the results in one of the Figures 1,2,4,5 in the main body of the paper Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods.

In particular

Figure 1: Simulations for subspace denoising
Figure 2: Image denoising
Figure 4: Compressive sensing for natural images
Figure 5: Compressive sensing accelerated MRI

Requirements

CUDA-enabled GPU is necessary to run the code. We tested this code using:

Ubuntu 20.04
CUDA 11.5
Python 3.7.11
PyTorch 1.10.0

Installation

First, install PyTorch 1.10.0 with CUDA support following the instructions here. Then, to install the necessary packages run

pip install -r requirements.txt

We used the bart toolbox to pre-compute the sensitivity maps for the experiments on accelerated MRI. Install bart toolbox by following the instructions on their home page.

Datasets

fastMRI

FastMRI is an open dataset, however you need to apply for access at https://fastmri.med.nyu.edu/. To run the experiments from our paper, you need to download the fastMRI brain dataset.

ImageNet

ImageNet is an open dataset, and you can request access at https://image-net.org/download.php. To run the experiments from our paper, you need to download the ImageNet train set.

Acknowledgments and references

The code for MRI reconstruction partly builds on the fastMRI repository, and the code for image denoising on Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks.

Klug et al. "Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods". In https://arxiv.org/abs/2305.19079 (2023).
Zbontar et al. "fastMRI: An Open Dataset and Benchmarks for Accelerated MRI". In: https://arxiv.org/abs/1811.08839* (2018).
Russakovsky et al. "ImageNet Large Scale Visual Recognition Challenge". In: International Journal of Computer Vision (2015).

MLI-lab / sample_complexity_ss_recon