# Image Deconvolution via Noise-Tolerant Self-Supervised Inversion

arXiv: https://arxiv.org/abs/2006.06156 · HTML: https://mlsanity.com/paper/2006.06156/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks](https://mlsanity.com/paper/2108.07481/) (2021)
- [Self-Supervised Single-Image Deconvolution with Siamese Neural Networks](https://mlsanity.com/paper/2308.09426/) (2023)
- [Physen-Noise2Noise: Physics-Guided Self-Supervised Defocus Deblurring with Bias Correction under Low-Light Conditions](https://mlsanity.com/paper/2605.24590/) (2026)

## Abstract (arXiv)

We propose a general framework for solving inverse problems in the presence of noise that requires no signal prior, no noise estimate, and no clean training data. We only require that the forward model be available and that the noise be statistically independent across measurement dimensions. We build upon the theory of J-invariant functions (Batson & Royer 2019, arXiv:1901.11365) and show how self-supervised denoising `a la Noise2Self is a special case of learning a noise-tolerant pseudo-inverse of the identity. We demonstrate our approach by showing how a convolutional neural network can be taught in a self-supervised manner to deconvolve images and surpass in image quality classical inversion schemes such as Lucy-Richardson deconvolution.