Phase retrieval (PR) is an important nonlinear inverse problem in scientific imaging. It aims at reconstructing the phase of a signal from its intensity measurements. Recently, there is an increasing interest in deep learning-based PR. Motivated by the challenge of collecting ground-truth (GT) images in many domains, this paper proposes an unsupervised learning approach for PR, which trains a deep end-to-end model without using any GT image. The unsupervised learning from intensity measurements is done via a teacher-student online distillation framework. A teacher model is trained using a self-expressive loss with noise resistance, and a student model is trained with a consistency loss on augmented data to exploit the teacher's dark knowledge for improvement. An enhanced unfolding neural network is also developed for both the teacher and student models. Extensive experiments show that, the proposed approach not only outperforms existing unsupervised PR methods with higher computational efficiency, but also performs competitively against the supervised ones.

Unsupervised Deep Learning for Phase Retrieval via Teacher-Student Distillation

CV: Computational Photography, Image & Video Synthesis 4

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Zhile Chen

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Unsupervised Deep Learning for Phase Retrieval via Teacher-Student Distillation

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