# High-Resolution Deep Image Matting

arXiv: https://arxiv.org/abs/2009.06613 · HTML: https://mlsanity.com/paper/2009.06613/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 30 of 36)

- [Highly Accurate Dichotomous Image Segmentation](https://mlsanity.com/paper/2203.03041/) (2022)
- [Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging](https://mlsanity.com/paper/2105.14021/) (2021)
- [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://mlsanity.com/paper/2305.15272/) (2023)
- [High-Resolution Image Harmonization via Collaborative Dual Transformations](https://mlsanity.com/paper/2109.06671/) (2021)
- [MatteFormer: Transformer-Based Image Matting via Prior-Tokens](https://mlsanity.com/paper/2203.15662/) (2022)
- [Matting Anything](https://mlsanity.com/paper/2306.05399/) (2023)
- [PP-Matting: High-Accuracy Natural Image Matting](https://mlsanity.com/paper/2204.09433/) (2022)
- [Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation](https://mlsanity.com/paper/2201.06889/) (2022)
- [VMFormer: End-to-End Video Matting with Transformer](https://mlsanity.com/paper/2208.12801/) (2022)
- [Convolutional Neural Opacity Radiance Fields](https://mlsanity.com/paper/2104.01772/) (2021)
- [TransMatting: Enhancing Transparent Objects Matting with Transformers](https://mlsanity.com/paper/2208.03007/) (2022)
- [Long-Range Feature Propagating for Natural Image Matting](https://mlsanity.com/paper/2109.12252/) (2021)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [Diffusion for Natural Image Matting](https://mlsanity.com/paper/2312.05915/) (2023)
- [Layered Depth Refinement with Mask Guidance](https://mlsanity.com/paper/2206.03048/) (2022)
- [Revisiting Context Aggregation for Image Matting](https://mlsanity.com/paper/2304.01171/) (2023)
- [Hierarchical and Progressive Image Matting](https://mlsanity.com/paper/2210.06906/) (2022)
- [dugMatting: Decomposed-Uncertainty-Guided Matting](https://mlsanity.com/paper/2306.01452/) (2023)
- [EFormer: Enhanced Transformer towards Semantic-Contour Features of Foreground for Portraits Matting](https://mlsanity.com/paper/2308.12831/) (2023)
- [Video Instance Matting](https://mlsanity.com/paper/2311.04212/) (2023)
- [Alpha Matte Generation from Single Input for Portrait Matting](https://mlsanity.com/paper/2106.03210/) (2021)
- [Situational Perception Guided Image Matting](https://mlsanity.com/paper/2204.09276/) (2022)
- [Investigating Neural Architectures by Synthetic Dataset Design](https://mlsanity.com/paper/2204.11045/) (2022)
- [Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human Matting](https://mlsanity.com/paper/2404.00921/) (2024)
- [Trimap-guided Feature Mining and Fusion Network for Natural Image Matting](https://mlsanity.com/paper/2112.00510/) (2021)
- [Improving Deep Image Matting via Local Smoothness Assumption](https://mlsanity.com/paper/2112.13809/) (2021)
- [Disentangled Pre-training for Image Matting](https://mlsanity.com/paper/2304.00784/) (2023)
- [DiffusionMat: Alpha Matting as Sequential Refinement Learning](https://mlsanity.com/paper/2311.13535/) (2023)
- [VRMDiff: Text-Guided Video Referring Matting Generation of Diffusion](https://mlsanity.com/paper/2503.10678/) (2025)
- [Overpainting: Localized Context-aware Diffusion Image Editing](https://mlsanity.com/paper/2609.10811/) (2026)

## Abstract (arXiv)

Image matting is a key technique for image and video editing and composition. Conventionally, deep learning approaches take the whole input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such approaches set state-of-the-arts in image matting; however, they may fail in real-world matting applications due to hardware limitations, since real-world input images for matting are mostly of very high resolution. In this paper, we propose HDMatt, a first deep learning based image matting approach for high-resolution inputs. More concretely, HDMatt runs matting in a patch-based crop-and-stitch manner for high-resolution inputs with a novel module design to address the contextual dependency and consistency issues between different patches. Compared with vanilla patch-based inference which computes each patch independently, we explicitly model the cross-patch contextual dependency with a newly-proposed Cross-Patch Contextual module (CPC) guided by the given trimap. Extensive experiments demonstrate the effectiveness of the proposed method and its necessity for high-resolution inputs. Our HDMatt approach also sets new state-of-the-art performance on Adobe Image Matting and AlphaMatting benchmarks and produce impressive visual results on more real-world high-resolution images.