这篇论文用几何先验替代隐式正则，参数更少但效果不输主流DEQ，做图像复原的可以看看。
该框架针对乘性Gamma噪声和模糊退化图像，采用深度平衡（DEQ）架构。与依赖隐式神经正则化的传统DEQ不同，该方法用表面积和平均曲率构造显式几何正则器。为优化变分模型，开发了适配Gamma噪声保真项的镜像下降算法，并利用o-minimal结构的Kurdyka-Lojasiewicz性质证明迭代全局收敛。在灰度与彩色图像上的实验表明，该方法胜过代表性模型方法，用远少于隐式正则化DEQ的参数达到相当性能。
A geometry-based deep equilibrium model for image restoration under multiplicative Gamma noise
We propose a deep learning framework for image restoration from images degraded by both multiplicative Gamma noise and blur. Unlike conventional deep equilibrium (DEQ) models that rely on implicit neural regularization, the proposed method learns an explicit and interpretable regularizer parameterized by geometric priors associated with surface area and mean curvature. To minimize the resulting variational model, we develop a mirror descent algorithm tailored to the commonly used Gamma-noise fidelity terms. Leveraging the Kurdyka-Lojasiewicz property for functions defined in $o$-minimal structures, we establish the global convergence of the generated iterates to a critical point. Experimental results on both grayscale and color image restoration demonstrate that the proposed method consistently outperforms representative model-based approaches while achieving performance comparable to state-of-the-art DEQ models based on implicit regularization, despite requiring substantially fewer trainable parameters.