# Deep Fusion Network for Image Completion

arXiv: https://arxiv.org/abs/1904.08060 · HTML: https://mlsanity.com/paper/1904.08060/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 17 of 17)

- [M3Care: Learning with Missing Modalities in Multimodal Healthcare Data](https://mlsanity.com/paper/2210.17292/) (2022)
- [Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2304.03322/) (2023)
- [ClearPose: Large-scale Transparent Object Dataset and Benchmark](https://mlsanity.com/paper/2203.03890/) (2022)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework](https://mlsanity.com/paper/2303.12400/) (2023)
- [Gradpaint: Gradient-Guided Inpainting with Diffusion Models](https://mlsanity.com/paper/2309.09614/) (2023)
- [Self-Supervised Point Cloud Completion via Inpainting](https://mlsanity.com/paper/2111.10701/) (2021)
- [New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound](https://mlsanity.com/paper/2211.02912/) (2022)
- [Contrastive Attention Network with Dense Field Estimation for Face Completion](https://mlsanity.com/paper/2112.10310/) (2021)
- [CMAMRNet: A Contextual Mask-Aware Network Enhancing Mural Restoration Through Comprehensive Mask Guidance](https://mlsanity.com/paper/2508.07140/) (2025)
- [HI-GAN: Hierarchical Inpainting GAN with Auxiliary Inputs for Combined RGB and Depth Inpainting](https://mlsanity.com/paper/2402.10334/) (2024)
- [Iterative Facial Image Inpainting Based on an Encoder-Generator Architecture](https://mlsanity.com/paper/2101.07036/) (2021)
- [Can No-reference features help in Full-reference image quality estimation?](https://mlsanity.com/paper/2203.00845/) (2022)
- [Deep Two-Stage High-Resolution Image Inpainting](https://mlsanity.com/paper/2104.13464/) (2021)
- [Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images](https://mlsanity.com/paper/2202.11377/) (2022)
- [A 3M-Hybrid Model for the Restoration of Unique Giant Murals: A Case Study on the Murals of Yongle Palace](https://mlsanity.com/paper/2309.06194/) (2023)
- [MGAN-CRCM: A Novel Multiple Generative Adversarial Network and Coarse-Refinement Based Cognizant Method for Image Inpainting](https://mlsanity.com/paper/2412.19000/) (2024)

## Abstract (arXiv)

Deep image completion usually fails to harmonically blend the restored image into existing content, especially in the boundary area. This paper handles with this problem from a new perspective of creating a smooth transition and proposes a concise Deep Fusion Network (DFNet). Firstly, a fusion block is introduced to generate a flexible alpha composition map for combining known and unknown regions. The fusion block not only provides a smooth fusion between restored and existing content, but also provides an attention map to make network focus more on the unknown pixels. In this way, it builds a bridge for structural and texture information, so that information can be naturally propagated from known region into completion. Furthermore, fusion blocks are embedded into several decoder layers of the network. Accompanied by the adjustable loss constraints on each layer, more accurate structure information are achieved. We qualitatively and quantitatively compare our method with other state-of-the-art methods on Places2 and CelebA datasets. The results show the superior performance of DFNet, especially in the aspects of harmonious texture transition, texture detail and semantic structural consistency. Our source code will be avaiable at: urlhttps://github.com/hughplay/DFNet