Abstract
Poisson-Gaussian noise describes the noise of various imaging systems thus the need of efficient algorithms for Poisson-Gaussian image restoration. Deep learning methods offer state-of-the-art performance but often require sensor-specific training when used in a supervised setting. A promising alternative is given by plug-and-play (PnP) methods, which consist in learning only a regularization through a denoiser, allowing to restore images from several sources with the same network. This paper introduces PG-DPIR, an efficient PnP method for high-count Poisson-Gaussian inverse problems, adapted from DPIR. While DPIR is designed for white Gaussian noise, a naive adaptation to Poisson-Gaussian noise leads to prohibitively slow algorithms due to the absence of a closed-form proximal operator. To address this, we adapt DPIR for the specificities of Poisson-Gaussian noise and propose in particular an efficient initialization of the gradient descent required for the proximal step that accelerates convergence by several orders of magnitude. Experiments are conducted on satellite image restoration and super-resolution problems. High-resolution realistic Pleiades images are simulated for the experiments, which demonstrate that PG-DPIR achieves state-of-the-art performance with improved efficiency, which seems promising for on-ground satellite processing chains.
Abstract (translated)
泊松-高斯噪声描述了各种成像系统中的噪声特性，因此需要高效的算法来解决泊松-高斯图像恢复问题。深度学习方法提供了最先进的性能，但当在监督设置下使用时通常需要特定传感器的训练。一种有前景的替代方案是由插件播放（PnP）方法提供的，这些方法仅通过去噪器学习正则化项，从而能够利用同一网络从多个来源恢复图像。本文介绍了PG-DPIR，这是一种高效的PnP方法，用于解决高计数泊松-高斯逆问题，基于DPIR进行了改进。虽然DPIR是为白色高斯噪声设计的，但直接将其应用于泊松-高斯噪声会导致算法运行速度极其缓慢，因为缺乏封闭形式的近似算子。为了应对这一挑战，我们针对Poisson-Gaussian噪声的特点对DPIR进行了调整，并特别提出了一种高效的梯度下降初始化方法，用于加速近似步骤中的收敛速度，提高了几个数量级的速度。实验在卫星图像恢复和超分辨率问题上进行。利用高分辨率的现实Pleiades图像模拟了实验数据，结果表明PG-DPIR实现了最先进的性能并提高了效率，这似乎对于地面卫星处理链来说前景广阔。
URL
https://arxiv.org/abs/2504.10375
