论文标题
图像通过耐噪声的自我监督反演进行反卷积
Image Deconvolution via Noise-Tolerant Self-Supervised Inversion
论文作者
论文摘要
我们提出了一个通用框架，以解决存在不需要信号之前的噪声，没有噪声估算和没有干净的训练数据的噪声。我们只要求向前模型可用，并且噪声在测量维度上统计独立。我们基于$ \ Mathcal {J} $ - 不变函数的理论（Batson＆Royer 2019，Arxiv：1901.11365），并展示如何自欺欺人的denoising \ emph {àlo} noings2self是一个特殊的案例，是学习具有噪音噪声的特殊情况。我们通过展示如何以自我监管的方式教授卷积神经网络来证明我们的方法，以解析图像并超过图像质量质量的经典反转方案，例如露西 - 里奇森（Lucy-Richardson）反卷积。
We propose a general framework for solving inverse problems in the presence of noise that requires no signal prior, no noise estimate, and no clean training data. We only require that the forward model be available and that the noise be statistically independent across measurement dimensions. We build upon the theory of $\mathcal{J}$-invariant functions (Batson & Royer 2019, arXiv:1901.11365) and show how self-supervised denoising \emph{à la} Noise2Self is a special case of learning a noise-tolerant pseudo-inverse of the identity. We demonstrate our approach by showing how a convolutional neural network can be taught in a self-supervised manner to deconvolve images and surpass in image quality classical inversion schemes such as Lucy-Richardson deconvolution.
