Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies. Yet, studying and understanding self-supervised learning for regression tasks - except for a particular regression task, image denoising - have lagged behind. This paper proposes a general self-supervised regression learning (SSRL) framework that enables learning regression neural networks with only input data (but without ground-truth target data), by using a designable pseudo-predictor that encapsulates domain knowledge of a specific application. The paper underlines the importance of using domain knowledge by showing that under different settings, the better pseudo-predictor can lead properties of SSRL closer to those of ordinary supervised learning. Numerical experiments for low-dose computational tomography denoising and camera image denoising demonstrate that proposed SSRL significantly improves the denoising quality over several existing self-supervised denoising methods.

翻译：预测连续量的回归是利用计算成像和计算机视觉技术应用的一个核心部分。然而,研究和理解自我监督的回归任务学习-除了特定的回归任务之外,图像去除-已经落后于以往。本文件提出一个一般性的自我监督回归学习(SSRL)框架,通过使用一种可设计化的假伪抑制器,将特定应用的域知识包罗起来,使学习回归神经网络能够学习,只有输入数据(但不包含地面真实目标数据),通过使用一种可设计化的伪抑制器,将特定应用的域知识包罗起来。本文强调使用域知识的重要性,通过显示在不同的环境下,更好的伪抑制剂可以使SSRL的特性更接近普通监督学习的特性。低剂量计算图解析和相机图像去除的数值实验表明,拟议的SSRL大大改进了现有若干自监督的自闭方法的去除质量。