October 5, 2025Open Access

Learning Regularization Functionals for Inverse Problems: A Comparative Study

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The unified framework reveals strengths and limitations of different learned regularization methods for inverse problems.
Numerous architectural designs and training strategies impact the effectiveness of learned regularization in imaging.
By consolidating code, this study facilitates systematic comparisons of learned regularization frameworks for inverse problems.
The findings provide valuable insights into future improvements in learned regularization techniques for imaging.

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This study demonstrates how unified frameworks can compare learned regularization approaches, highlighting strengths and limitations.

Hertrich et al. (2025) studied this question.

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