# DeepLens: Shallow Depth Of Field From A Single Image

arXiv: https://arxiv.org/abs/1810.08100 · HTML: https://mlsanity.com/paper/1810.08100/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [3D Common Corruptions and Data Augmentation](https://mlsanity.com/paper/2203.01441/) (2022)
- [Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image](https://mlsanity.com/paper/2105.07174/) (2021)
- [PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation](https://mlsanity.com/paper/2206.00468/) (2022)
- [AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields](https://mlsanity.com/paper/2206.06100/) (2022)
- [DC2: Dual-Camera Defocus Control by Learning to Refocus](https://mlsanity.com/paper/2304.03285/) (2023)
- [Neural Camera Simulators](https://mlsanity.com/paper/2104.05237/) (2021)
- [Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography](https://mlsanity.com/paper/2212.12324/) (2022)
- [Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors](https://mlsanity.com/paper/2306.04506/) (2023)
- [DiffCamera: Arbitrary Refocusing on Images](https://mlsanity.com/paper/2509.26599/) (2025)
- [Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks](https://mlsanity.com/paper/2106.13041/) (2021)
- [Adaptive Mask-based Pyramid Network for Realistic Bokeh Rendering](https://mlsanity.com/paper/2210.16078/) (2022)
- [AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment](https://mlsanity.com/paper/2301.09315/) (2023)
- [Parameter-Free Neural Lens Blur Rendering for High-Fidelity Composites](https://mlsanity.com/paper/2511.17014/) (2025)

## Abstract (arXiv)

We aim to generate high resolution shallow depth-of-field (DoF) images from a single all-in-focus image with controllable focal distance and aperture size. To achieve this, we propose a novel neural network model comprised of a depth prediction module, a lens blur module, and a guided upsampling module. All modules are differentiable and are learned from data. To train our depth prediction module, we collect a dataset of 2462 RGB-D images captured by mobile phones with a dual-lens camera, and use existing segmentation datasets to improve border prediction. We further leverage a synthetic dataset with known depth to supervise the lens blur and guided upsampling modules. The effectiveness of our system and training strategies are verified in the experiments. Our method can generate high-quality shallow DoF images at high resolution, and produces significantly fewer artifacts than the baselines and existing solutions for single image shallow DoF synthesis. Compared with the iPhone portrait mode, which is a state-of-the-art shallow DoF solution based on a dual-lens depth camera, our method generates comparable results, while allowing for greater flexibility to choose focal points and aperture size, and is not limited to one capture setup.