# Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2night

arXiv: https://arxiv.org/abs/1908.10526 · HTML: https://mlsanity.com/paper/1908.10526/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [SPT: Sequence Prompt Transformer for Interactive Image Segmentation](https://mlsanity.com/paper/2412.10224/) (2024)
- [Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Models](https://mlsanity.com/paper/2406.04388/) (2024)
- [Disharmony: Forensics using Reverse Lighting Harmonization](https://mlsanity.com/paper/2501.10212/) (2025)
- [T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs](https://mlsanity.com/paper/2511.16107/) (2025)
- [CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences](https://mlsanity.com/paper/2606.00931/) (2026)

## Abstract (arXiv)

Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image. Image harmonization, which aims to make the foreground compatible with the background, is a promising yet challenging task. However, the lack of high-quality public dataset for image harmonization, which significantly hinders the development of image harmonization techniques. Therefore, we contribute an image harmonization dataset iHarmony4 by generating synthesized composite images based on existing COCO (resp., Adobe5k, day2night) dataset, leading to our HCOCO (resp., HAdobe5k, Hday2night) sub-dataset. To enrich the diversity of our dataset, we also generate synthesized composite images based on our collected Flick images, leading to our HFlickr sub-dataset. The image harmonization dataset iHarmony4 is released at https://github.com/bcmi/Image_Harmonization_Datasets.