# Improving Deep Image Matting via Local Smoothness Assumption

arXiv: https://arxiv.org/abs/2112.13809 · HTML: https://mlsanity.com/paper/2112.13809/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 27 Dec 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://mlsanity.com/paper/1904.09730/) (2019)
- [Deep Image Matting](https://mlsanity.com/paper/1703.03872/) (2017)
- [f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation](https://mlsanity.com/paper/2001.10331/) (2020)
- [Background Matting: The World is Your Green Screen](https://mlsanity.com/paper/2004.00626/) (2020)
- [Indices Matter: Learning to Index for Deep Image Matting](https://mlsanity.com/paper/1908.00672/) (2019)
- [Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation](https://mlsanity.com/paper/1909.09725/) (2019)
- [Natural Image Matting via Guided Contextual Attention](https://mlsanity.com/paper/2001.04069/) (2020)
- [Information-Flow Matting](https://mlsanity.com/paper/1707.05055/) (2017)
- [Semantic Image Matting](https://mlsanity.com/paper/2104.08201/) (2021)
- [AlphaGAN: Generative adversarial networks for natural image matting](https://mlsanity.com/paper/1807.10088/) (2018)
- [F, B, Alpha Matting](https://mlsanity.com/paper/2003.07711/) (2020)
- [Disentangled Image Matting](https://mlsanity.com/paper/1909.04686/) (2019)
- [Learning Affinity-Aware Upsampling for Deep Image Matting](https://mlsanity.com/paper/2011.14288/) (2020)
- [High-Resolution Deep Image Matting](https://mlsanity.com/paper/2009.06613/) (2020)
- [Long-Range Feature Propagating for Natural Image Matting](https://mlsanity.com/paper/2109.12252/) (2021)
- [Fast Multi-Level Foreground Estimation](https://mlsanity.com/paper/2006.14970/) (2020)

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

- [POBEVM: Real-time Video Matting via Progressively Optimize the Target Body and Edge](https://mlsanity.com/paper/2402.09731/) (2024)
- [Dual-Context Aggregation for Universal Image Matting](https://mlsanity.com/paper/2402.18109/) (2024)

## Abstract (arXiv)

Natural image matting is a fundamental and challenging computer vision task. Conventionally, the problem is formulated as an underconstrained problem. Since the problem is ill-posed, further assumptions on the data distribution are required to make the problem well-posed. For classical matting methods, a commonly adopted assumption is the local smoothness assumption on foreground and background colors. However, the use of such assumptions was not systematically considered for deep learning based matting methods. In this work, we consider two local smoothness assumptions which can help improving deep image matting models. Based on the local smoothness assumptions, we propose three techniques, i.e., training set refinement, color augmentation and backpropagating refinement, which can improve the performance of the deep image matting model significantly. We conduct experiments to examine the effectiveness of the proposed algorithm. The experimental results show that the proposed method has favorable performance compared with existing matting methods.