用神经网络学习图像去噪的自适应参数图，提升未知噪声下的去噪效果。
Patch-based learning of adaptive Total Variation parameter maps for blind image denoising
- 基于滑动窗口的局部块学习，动态优化各像素的总变差权重。
- 在高斯和泊松噪声下均显著优于固定参数的去噪方法。
- 适合处理噪声分布未知的真实图像去噪任务。
本文提出一种基于神经网络的块级学习方法，用于估计图像去噪中加权总变差（TV）的时空自适应正则化参数图，适用于噪声分布未知的情况。以高斯或泊松噪声为例，先通过标准二分类网络进行初步模型选择；随后采用滑动窗口策略，在参考自然图像块上监督学习每个像素的最优TV正则项与数据保真项之间的权衡，以最大化结构相似性（SSIM）。大量数值实验表明，该方法在两种噪声模型下均显著优于最优标量正则化方案。
原文摘要 · Abstract (English)
We consider a patch-based learning approach defined in terms of neural networks to estimate spatially adaptive regularisation parameter maps for image denoising with weighted Total Variation (TV) and test it to situations when the noise distribution is unknown. As an example, we consider situations where noise could be either Gaussian or Poisson and perform preliminary model selection by a standard binary classification network. Then, we define a patch-based approach where at each image pixel an optimal weighting between TV regularisation and the corresponding data fidelity is learned in a supervised way using reference natural image patches upon optimisation of SSIM and in a sliding window fashion. Extensive numerical results are reported for both noise models, showing significant improvement w.r.t. results obtained by means of optimal scalar regularisation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。