# Learning to Estimate Kernel Scale and Orientation of Defocus Blur with Asymmetric Coded Aperture

arXiv: https://arxiv.org/abs/2103.05843 · HTML: https://mlsanity.com/paper/2103.05843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Image and Depth from a Single Defocused Image Using Coded Aperture Photography](https://mlsanity.com/paper/1603.04046/) (2016)

## Abstract (arXiv)

Consistent in-focus input imagery is an essential precondition for machine vision systems to perceive the dynamic environment. A defocus blur severely degrades the performance of vision systems. To tackle this problem, we propose a deep-learning-based framework estimating the kernel scale and orientation of the defocus blur to adjust lens focus rapidly. Our pipeline utilizes 3D ConvNet for a variable number of input hypotheses to select the optimal slice from the input stack. We use random shuffle and Gumbel-softmax to improve network performance. We also propose to generate synthetic defocused images with various asymmetric coded apertures to facilitate training. Experiments are conducted to demonstrate the effectiveness of our framework.