无需参考图像即可自动优化去噪参数，提升图像恢复效果。
Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising
- 基于残差白化损失的双层优化，自动学习像素级参数图
- 在自然图像上实现与监督方法相当的去噪性能
- 适合无参考图像、噪声未知场景的图像去噪应用
本文提出一种基于归一化残差白化损失的双层优化策略，用于估计加性高斯白噪声图像去噪中的加权全变分参数图。与依赖参考数据或噪声强度先验的监督/半监督方法不同，该方法完全无监督。为防止噪声过拟合，采用基于自然图像最优性能统计的早停策略。数值实验对比了标量与像素依赖型参数图在监督与无监督设置下的表现，验证了方法的有效性。
原文摘要 · Abstract (English)
We consider a bilevel optimisation strategy based on normalised residual whiteness loss for estimating the weighted total variation parameter maps for denoising images corrupted by additive white Gaussian noise. Compared to supervised and semi-supervised approaches relying on prior knowledge of (approximate) reference data and/or information on the noise magnitude, the proposal is fully unsupervised. To avoid noise overfitting an early stopping strategy is used, relying on simple statistics of optimal performances on a set of natural images. Numerical results comparing the supervised/unsupervised procedures for scalar/pixel-dependent \mbox{parameter maps are shown.
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