# Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus

arXiv: https://arxiv.org/abs/2402.18175 · HTML: https://mlsanity.com/paper/2402.18175/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Focus on defocus: bridging the synthetic to real domain gap for depth estimation](https://mlsanity.com/paper/2005.09623/) (2020)
- [Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision](https://mlsanity.com/paper/2108.10843/) (2021)
- [Deep Depth from Focus with Differential Focus Volume](https://mlsanity.com/paper/2112.01712/) (2021)
- [Aberration-Aware Depth-from-Focus](https://mlsanity.com/paper/2303.04654/) (2023)

## Built on by (most cited first, 2 of 2)

- [Dark Channel-Assisted Depth-from-Defocus from a Single Image](https://mlsanity.com/paper/2506.06643/) (2025)
- [Large-FOV RGBD Imaging via Structured PSF Coding in a Bio-inspired Monocentric System](https://mlsanity.com/paper/2510.25314/) (2025)

## Abstract (arXiv)

In this paper, we address the task of aberration-aware depth-from-defocus (DfD), which takes account of spatially variant point spread functions (PSFs) of a real camera. To effectively obtain the spatially variant PSFs of a real camera without requiring any ground-truth PSFs, we propose a novel self-supervised learning method that leverages the pair of real sharp and blurred images, which can be easily captured by changing the aperture setting of the camera. In our PSF estimation, we assume rotationally symmetric PSFs and introduce the polar coordinate system to more accurately learn the PSF estimation network. We also handle the focus breathing phenomenon that occurs in real DfD situations. Experimental results on synthetic and real data demonstrate the effectiveness of our method regarding both the PSF estimation and the depth estimation.