# Direct Handheld Burst Imaging to Simulated Defocus

arXiv: https://arxiv.org/abs/2207.04175 · HTML: https://mlsanity.com/paper/2207.04175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 9 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Learning-Based View Synthesis for Light Field Cameras](https://mlsanity.com/paper/1609.02974/) (2016)
- [Handheld Multi-Frame Super-Resolution](https://mlsanity.com/paper/1905.03277/) (2019)
- [Synthetic Depth-of-Field with a Single-Camera Mobile Phone](https://mlsanity.com/paper/1806.04171/) (2018)
- [Learning to Synthesize a 4D RGBD Light Field from a Single Image](https://mlsanity.com/paper/1708.03292/) (2017)
- [Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image](https://mlsanity.com/paper/2105.07174/) (2021)

## Abstract (arXiv)

A shallow depth-of-field image keeps the subject in focus, and the foreground and background contexts blurred. This effect requires much larger lens apertures than those of smartphone cameras. Conventional methods acquire RGB-D images and blur image regions based on their depth. However, this approach is not suitable for reflective or transparent surfaces, or finely detailed object silhouettes, where the depth value is inaccurate or ambiguous. We present a learning-based method to synthesize the defocus blur in shallow depth-of-field images from handheld bursts acquired with a single small aperture lens. Our deep learning model directly produces the shallow depth-of-field image, avoiding explicit depth-based blurring. The simulated aperture diameter equals the camera translation during burst acquisition. Our method does not suffer from artifacts due to inaccurate or ambiguous depth estimation, and it is well-suited to portrait photography.