用梯度域去噪器替代图像域，提升图像逆问题重建效果
Analysis Plug-and-Play Methods for Imaging Inverse Problems
- 在梯度域训练去噪器，实现学习型总变差正则化
- 两种新算法在去模糊和超分辨率任务中表现接近传统方法
- 适合需要高细节重建的图像恢复场景
插件式先验（PnP）框架通过将训练好的去噪器作为自然图像的隐式先验，广泛应用于图像逆问题求解。本文提出一种分析型PnP方法，将先验施加于图像的变换表示（如梯度域），而非直接在图像域操作。具体地，训练一个高斯去噪器在梯度域工作，从而实现学习型总变差（TV）正则化。为融合该梯度域先验，我们提出了基于半二次分裂（APnP-HQS）和交替方向乘子法（APnP-ADMM）的两种算法。在图像去模糊与超分辨率任务上的实验表明，该分析形式性能可媲美传统图像域PnP方法。
原文摘要 · Abstract (English)
Plug-and-Play Priors (PnP) is a popular framework for solving imaging inverse problems by integrating learned priors in the form of denoisers trained to remove Gaussian noise from images. In standard PnP methods, the denoiser is applied directly in the image domain, serving as an implicit prior on natural images. This paper considers an alternative analysis formulation of PnP, in which the prior is imposed on a transformed representation of the image, such as its gradient. Specifically, we train a Gaussian denoiser to operate in the gradient domain, rather than on the image itself. Conceptually, this is an extension of total variation (TV) regularization to learned TV regularization. To incorporate this gradient-domain prior in image reconstruction algorithms, we develop two analysis PnP algorithms based on half-quadratic splitting (APnP-HQS) and the alternating direction method of multipliers (APnP-ADMM). We evaluate our approach on image deblurring and super-resolution, demonstrating that the analysis formulation achieves performance comparable to image-domain PnP algorithms.
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