Single-photon Image Super-resolution via Self-supervised Learning
This addresses the challenge of expensive or unavailable training pairs in computational imaging for real-world applications, though it is incremental as it extends existing self-supervised techniques to a specific domain.
The paper tackles the problem of single-photon image super-resolution without requiring paired training data, by proposing a self-supervised learning framework based on Equivariant Imaging and a Poisson unbiased Kullback-Leibler risk estimator, achieving comparable performance to supervised methods and outperforming interpolation-based approaches.
Single-Photon Image Super-Resolution (SPISR) aims to recover a high-resolution volumetric photon counting cube from a noisy low-resolution one by computational imaging algorithms. In real-world scenarios, pairs of training samples are often expensive or impossible to obtain. By extending Equivariant Imaging (EI) to volumetric single-photon data, we propose a self-supervised learning framework for the SPISR task. Particularly, using the Poisson unbiased Kullback-Leibler risk estimator and equivariance, our method is able to learn from noisy measurements without ground truths. Comprehensive experiments on simulated and real-world dataset demonstrate that the proposed method achieves comparable performance with supervised learning and outperforms interpolation-based methods.