Equivariance Regularization for Image Reconstruction

Junqi Tang

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arXiv · 2202.05062

Equivariance Regularization for Image Reconstruction

Junqi Tang

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Abstract

In this work, we propose Regularization-by-Equivariance (REV), a novel structure-adaptive regularization scheme for solving imaging inverse problems under incomplete measurements. This regularization scheme utilizes the equivariant structure in the physics of the measurements -- which is prevalent in many inverse problems such as tomographic image reconstruction -- to mitigate the ill-poseness of the inverse problem. Our proposed scheme can be applied in a plug-and-play manner alongside with any classic first-order optimization algorithm such as the accelerated gradient descent/FISTA for simplicity and fast convergence. The numerical experiments in sparse-view X-ray CT image reconstruction tasks demonstrate the effectiveness of our approach.

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BibTeX RIS

Junqi Tang. 2022-02-10. Equivariance Regularization for Image Reconstruction. https://arxiv.org/abs/2202.05062

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