Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring
This addresses the challenge of learning from low-frequency measurements in imaging systems where ground truth data is scarce, offering a novel solution for scientific and medical applications.
The paper tackled the problem of image super-resolution and deblurring by proposing a scale-equivariant self-supervised learning method, which outperformed other self-supervised approaches and achieved performance on par with fully supervised learning on real datasets.
Self-supervised methods have recently proved to be nearly as effective as supervised ones in various imaging inverse problems, paving the way for learning-based approaches in scientific and medical imaging applications where ground truth data is hard or expensive to obtain. These methods critically rely on invariance to translations and/or rotations of the image distribution to learn from incomplete measurement data alone. However, existing approaches fail to obtain competitive performances in the problems of image super-resolution and deblurring, which play a key role in most imaging systems. In this work, we show that invariance to roto-translations is insufficient to learn from measurements that only contain low-frequency information. Instead, we propose scale-equivariant imaging, a new self-supervised approach that leverages the fact that many image distributions are approximately scale-invariant, enabling the recovery of high-frequency information lost in the measurement process. We demonstrate throughout a series of experiments on real datasets that the proposed method outperforms other self-supervised approaches, and obtains performances on par with fully supervised learning.