# Learning Global-aware Kernel for Image Harmonization

arXiv: https://arxiv.org/abs/2305.11676 · HTML: https://mlsanity.com/paper/2305.11676/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 53 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Dynamic Filter Networks](https://mlsanity.com/paper/1605.09673/) (2016)
- [CondConv: Conditionally Parameterized Convolutions for Efficient Inference](https://mlsanity.com/paper/1904.04971/) (2019)
- [Video Frame Interpolation via Adaptive Convolution](https://mlsanity.com/paper/1703.07514/) (2017)
- [Burst Denoising with Kernel Prediction Networks](https://mlsanity.com/paper/1712.02327/) (2017)
- [DoveNet: Deep Image Harmonization via Domain Verification](https://mlsanity.com/paper/1911.13239/) (2019)
- [High-Fidelity Pluralistic Image Completion with Transformers](https://mlsanity.com/paper/2103.14031/) (2021)
- [Deep Image Harmonization](https://mlsanity.com/paper/1703.00069/) (2017)
- [Attentional Feature Fusion](https://mlsanity.com/paper/2009.14082/) (2020)
- [FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting](https://mlsanity.com/paper/2109.02974/) (2021)
- [Auto-Exposure Fusion for Single-Image Shadow Removal](https://mlsanity.com/paper/2103.01255/) (2021)
- [Improving the Harmony of the Composite Image by Spatial-Separated Attention Module](https://mlsanity.com/paper/1907.06406/) (2019)
- [Region-aware Adaptive Instance Normalization for Image Harmonization](https://mlsanity.com/paper/2106.02853/) (2021)
- [Harmonizer: Learning to Perform White-Box Image and Video Harmonization](https://mlsanity.com/paper/2207.01322/) (2022)
- [Foreground-aware Semantic Representations for Image Harmonization](https://mlsanity.com/paper/2006.00809/) (2020)
- [BargainNet: Background-Guided Domain Translation for Image Harmonization](https://mlsanity.com/paper/2009.09169/) (2020)
- [DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization](https://mlsanity.com/paper/2207.04788/) (2022)
- [ExFuse: Enhancing Feature Fusion for Semantic Segmentation](https://mlsanity.com/paper/1804.03821/) (2018)
- [EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining](https://mlsanity.com/paper/2009.09238/) (2020)
- [Learning to Synthesize Motion Blur](https://mlsanity.com/paper/1811.11745/) (2018)

## Built on by (most cited first, 4 of 4)

- [Segment Anything Model Meets Image Harmonization](https://mlsanity.com/paper/2312.12729/) (2023)
- [Diverse Image Harmonization](https://mlsanity.com/paper/2407.15481/) (2024)
- [Region-to-Region: Enhancing Generative Image Harmonization with Adaptive Regional Injection](https://mlsanity.com/paper/2508.09746/) (2025)
- [Visually Grounded Narratives: Reducing Cognitive Burden in Researcher-Participant Interaction](https://mlsanity.com/paper/2509.00381/) (2025)

## Abstract (arXiv)

Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing methods employ local color transformation or region matching between foreground and background, which neglects powerful proximity prior and independently distinguishes fore-/back-ground as a whole part for harmonization. As a result, they still show a limited performance across varied foreground objects and scenes. To address this issue, we propose a novel Global-aware Kernel Network (GKNet) to harmonize local regions with comprehensive consideration of long-distance background references. Specifically, GKNet includes two parts, ie, harmony kernel prediction and harmony kernel modulation branches. The former includes a Long-distance Reference Extractor (LRE) to obtain long-distance context and Kernel Prediction Blocks (KPB) to predict multi-level harmony kernels by fusing global information with local features. To achieve this goal, a novel Selective Correlation Fusion (SCF) module is proposed to better select relevant long-distance background references for local harmonization. The latter employs the predicted kernels to harmonize foreground regions with both local and global awareness. Abundant experiments demonstrate the superiority of our method for image harmonization over state-of-the-art methods, eg, achieving 39.53dB PSNR that surpasses the best counterpart by +0.78dB uparrow; decreasing fMSE/MSE by 11.5%downarrow/6.7%downarrow compared with the SoTA method. Code will be available at hrefhttps://github.com/XintianShen/GKNethere.