Benchmark on Inference for Large-Scale Inverse Problems
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Welcome to the Benchmark on Inference for Large-Scale Inverse Problems — a `BenchOpt <https://benchopt.github.io>`_ benchmark for large scale inverse problems resolution based on  `DeepInv <https://deepinv.github.io>`_.

Overview
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This benchmark evaluates reconstruction algorithms for large-scale inverse problems across multiple imaging modalities. 
In inverse problems, we aim to recover the original signal :math:`x` from measurements :math:`y` following the model:

.. math::

   y = Ax + n

where :math:`A` is the forward operator (e.g., blur, tomographic projection) and :math:`n` represents noise.

**Datasets**

The benchmark includes three imaging scenarios:

- **Tomography (2D/3D):** Computed tomography reconstruction from multiple projection operators
- **High-Resolution Color Images:** Image restoration from multiple anisotropic Gaussian blur operators


These datasets are multi-operator problems: from a single ground truth, we observe different measurements (e.g., tomography uses different projection angles; natural images use different blur kernels). The goal is to recover the original image from these measurements. 
The benchmark focuses on large scale images or volumes, of order of magnitude from 1 to 100 million pixels/voxels.

**Reconstruction Methods**

We leverage the `DeepInv <https://deepinv.github.io>`_ library to implement distributed resolution algorithms:

- **Plug-and-Play (PnP):** Combines data-fidelity optimization with pretrained denoisers as image priors, offering flexibility and strong performance without task-specific training


**Evaluation Conditions**

The benchmark assesses solver performance under varying configurations:

- **Image sizes:** Testing across different resolution scales
- **Computational resources:** From single GPU to multi-GPU distributed setups

Performance is measured through reconstruction quality (PSNR) and computational efficiency (runtime, memory usage).

.. toctree::
   :hidden:
   :maxdepth: 2

   getting_started/index
   examples/index
   takeaways/index

Quick Links
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- **Get Started:** See :doc:`getting_started/quickstart` for a quick setup guide.
- **Examples:**  Explore :doc:`examples/index` for detailed, dataset-specific examples.
- **Key Takeaways:** Check out :doc:`takeaways/index` for a summary of benchmark insights and best practices.