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

Autor:	Chun, Il Yong, Park, Dongwon, Zheng, Xuehang, Chun, Se Young, Long, Yong
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 17 pages, 16 figures, 2 tables, submitted to IEEE T-IP
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.04821 Zobrazit plný text záznamu View this record from Arxiv