通过自适应权重提升图像重建精度，同时保持模型可解释性。
Learning Spatially Adaptive $\ell_1$-Norms Weights for Convolutional Synthesis Regularization
- 用展开算法学习卷积滤波器的局部自适应权重
- 在低场MRI重建中达到与先进方法相当的视觉和定量效果
- 参数图揭示滤波器贡献，便于分析与优化
我们提出一种基于卷积合成的ℓ₁正则化框架下的展开算法，用于学习空间自适应的参数映射。具体而言，基于一组预训练卷积滤波器，通过展开FISTA算法求解稀疏估计问题，推断深度参数化的空间可变参数，应用于稀疏特征图。该方法在低场磁共振成像（low-field MRI）重建任务中进行了评估，并与依赖总变差（Total Variation）正则化的空间自适应及非自适应分析型方法、以及成熟的模型驱动深度学习方法进行了对比。结果表明，所提方法在视觉质量和定量指标上均与现有方法相当，且具有高度可解释性。特别地，推断出的参数映射量化了各滤波器在重建中的局部贡献，为理解算法机制提供关键洞察，未来或可用于剔除不合适的滤波器。
原文摘要 · Abstract (English)
We propose an unrolled algorithm approach for learning spatially adaptive parameter maps in the framework of convolutional synthesis-based $\ell_1$ regularization. More precisely, we consider a family of pre-trained convolutional filters and estimate deeply parametrized spatially varying parameters applied to the sparse feature maps by means of unrolling a FISTA algorithm to solve the underlying sparse estimation problem. The proposed approach is evaluated for image reconstruction of low-field MRI and compared to spatially adaptive and non-adaptive analysis-type procedures relying on Total Variation regularization and to a well-established model-based deep learning approach. We show that the proposed approach produces visually and quantitatively comparable results with the latter approaches and at the same time remains highly interpretable. In particular, the inferred parameter maps quantify the local contribution of each filter in the reconstruction, which provides valuable insight into the algorithm mechanism and could potentially be used to discard unsuited filters.
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