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Regression that predicts continuous quantity is a central part ofapplications 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-predictorthat encapsulates domain knowledge of a specific application. The paperunderlines the importance of using domain knowledge by showing that underdifferent settings, the better pseudo-predictor can lead properties of SSRLcloser to those of ordinary supervised learning. Numerical experiments forlow-dose computational tomography denoising and camera image denoisingdemonstrate that proposed SSRL significantly improves the denoising qualityover several existing self-supervised denoising methods.

Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging