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

• URL: http://arxiv.org/abs/2205.04821v1
• Date: Tue, 10 May 2022 11:46:10 GMT
• Title: Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
• Authors: Il Yong Chun, Dongwon Park, Xuehang Zheng, Se Young Chun, Yong Long
• Abstract summary: This paper proposes a general self-supervised regression learning (SSRL) framework that enables learning regression neural networks with only input data. Numerical experiments for low-dose computational tomography denoising and camera image denoising demonstrate that proposed SSRL significantly improves the denoising quality.
• Score: 27.34785258514146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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