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arXiv · 2502.04079

DEALing with Image Reconstruction: Deep Attentive Least Squares

Mehrsa Pourya·Erich Kobler·Michael Unser·Sebastian Neumayer
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Abstract

State-of-the-art image reconstruction often relies on complex, highly parameterized deep architectures. We propose an alternative: a data-driven reconstruction method inspired by the classic Tikhonov regularization. Our approach iteratively refines intermediate reconstructions by solving a sequence of quadratic problems. These updates have two key components: (i) learned filters to extract salient image features, and (ii) an attention mechanism that locally adjusts the penalty of filter responses. Our method achieves performance on par with leading plug-and-play and learned regularizer approaches while offering interpretability, robustness, and convergent behavior. In effect, we bridge traditional regularization and deep learning with a principled reconstruction approach.

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Mehrsa Pourya, Erich Kobler, Michael Unser, Sebastian Neumayer. 2025-02-06. DEALing with Image Reconstruction: Deep Attentive Least Squares. https://arxiv.org/abs/2502.04079

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