# BargainNet: Background-Guided Domain Translation for Image Harmonization

arXiv: https://arxiv.org/abs/2009.09169 · HTML: https://mlsanity.com/paper/2009.09169/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2020.

## Built on by (most cited first, 30 of 52)

- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [Harmonizer: Learning to Perform White-Box Image and Video Harmonization](https://mlsanity.com/paper/2207.01322/) (2022)
- [High-Resolution Image Harmonization via Collaborative Dual Transformations](https://mlsanity.com/paper/2109.06671/) (2021)
- [DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization](https://mlsanity.com/paper/2207.04788/) (2022)
- [ObjectStitch: Generative Object Compositing](https://mlsanity.com/paper/2212.00932/) (2022)
- [Neural Preset for Color Style Transfer](https://mlsanity.com/paper/2303.13511/) (2023)
- [Shadow Generation for Composite Image in Real-world Scenes](https://mlsanity.com/paper/2104.10338/) (2021)
- [Collage Diffusion](https://mlsanity.com/paper/2303.00262/) (2023)
- [Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization](https://mlsanity.com/paper/2109.05750/) (2021)
- [SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization](https://mlsanity.com/paper/2204.13962/) (2022)
- [Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model](https://mlsanity.com/paper/2306.07596/) (2023)
- [Learning Object Placement via Dual-path Graph Completion](https://mlsanity.com/paper/2207.11464/) (2022)
- [Dense Pixel-to-Pixel Harmonization via Continuous Image Representation](https://mlsanity.com/paper/2303.01681/) (2023)
- [Shadow Generation for Composite Image Using Diffusion model](https://mlsanity.com/paper/2403.15234/) (2024)
- [FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior](https://mlsanity.com/paper/2407.04947/) (2024)
- [Cross-domain Compositing with Pretrained Diffusion Models](https://mlsanity.com/paper/2302.10167/) (2023)
- [Hierarchical Dynamic Image Harmonization](https://mlsanity.com/paper/2211.08639/) (2022)
- [LEMaRT: Label-Efficient Masked Region Transform for Image Harmonization](https://mlsanity.com/paper/2304.13166/) (2023)
- [Painterly Image Harmonization using Diffusion Model](https://mlsanity.com/paper/2308.02228/) (2023)
- [Deep Image Harmonization in Dual Color Spaces](https://mlsanity.com/paper/2308.02813/) (2023)
- [Deep Image Harmonization by Bridging the Reality Gap](https://mlsanity.com/paper/2103.17104/) (2021)
- [Painterly Image Harmonization in Dual Domains](https://mlsanity.com/paper/2212.08846/) (2022)
- [ORIDa: Object-centric Real-world Image Composition Dataset](https://mlsanity.com/paper/2506.08964/) (2025)
- [Deep Video Harmonization with Color Mapping Consistency](https://mlsanity.com/paper/2205.00687/) (2022)
- [FRIH: Fine-grained Region-aware Image Harmonization](https://mlsanity.com/paper/2205.06448/) (2022)
- [Image Harmonization by Matching Regional References](https://mlsanity.com/paper/2204.04715/) (2022)
- [Semantic-guided Multi-Mask Image Harmonization](https://mlsanity.com/paper/2207.11722/) (2022)
- [Foreground Object Search by Distilling Composite Image Feature](https://mlsanity.com/paper/2308.04990/) (2023)
- [Deep Image Harmonization with Learnable Augmentation](https://mlsanity.com/paper/2308.00376/) (2023)
- [Learning Global-aware Kernel for Image Harmonization](https://mlsanity.com/paper/2305.11676/) (2023)

## Abstract (arXiv)

Image composition is a fundamental operation in image editing field. However, unharmonious foreground and background downgrade the quality of composite image. Image harmonization, which adjusts the foreground to improve the consistency, is an essential yet challenging task. Previous deep learning based methods mainly focus on directly learning the mapping from composite image to real image, while ignoring the crucial guidance role that background plays. In this work, with the assumption that the foreground needs to be translated to the same domain as background, we formulate image harmonization task as background-guided domain translation. Therefore, we propose an image harmonization network with a novel domain code extractor and well-tailored triplet losses, which could capture the background domain information to guide the foreground harmonization. Extensive experiments on the existing image harmonization benchmark demonstrate the effectiveness of our proposed method. Code is available at https://github.com/bcmi/BargainNet.