Image enhancement approaches often assume that the noise is signal independent, and approximate the degradation model as zero-mean additive Gaussian. However, this assumption does not hold for biomedical imaging systems where sensor-based sources of noise are proportional to signal strengths, and the noise is better represented as a Poisson process. In this work, we explore a sparsity and dictionary learning-based approach and present a novel self-supervised learning method for single-image denoising where the noise is approximated as a Poisson process, requiring no clean ground-truth data. Specifically, we approximate traditional iterative optimization algorithms for image denoising with a recurrent neural network that enforces sparsity with respect to the weights of the network. Since the sparse representations are based on the underlying image, it is able to suppress the spurious components (noise) in the image patches, thereby introducing implicit regularization for denoising tasks through the network structure. Experiments on two bio-imaging datasets demonstrate that our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM. Our qualitative results demonstrate that, in addition to higher performance on standard quantitative metrics, we are able to recover much more subtle details than other compared approaches. Our code is made publicly available at https://github.com/tacalvin/Poisson2Sparse

翻译：提升图像的方法往往假定噪音是独立的信号,并且将降解模型大约为零中添加物高斯。然而,这一假设对于生物医学成像系统并不持有,因为基于传感器的噪音源与信号强度成比例,而且噪音作为Poisson进程代表的更好。在这项工作中,我们探索了一种以广度和字典学习为基础的方法,并提出了一种全新的自我监督的学习方法,在噪音被近似为Poisson进程的地方进行单一图像脱色,不需要干净的地面真相数据。具体地说,我们把传统的图像脱色迭代优化算法与一个经常的神经网络相近一些,而这个网络的神经网络对网络的重量施加了松散度。由于微量的表示是基于基本图像,因此我们能够抑制图像补接合处中的有色成分(噪音),从而对通过网络结构进行脱色任务进行隐含的规范化。在两个生物成像数据集上进行的实验表明,我们的方法在PSNR和SSIM2方面超越了状态的状态。我们的定性方法,我们的质量结果比公共标准要高得多地显示我们用来恢复。