Image Harmonization
25 papers with code • 2 benchmarks • 1 datasets
Image harmonization aims to modify the color of the composited region with respect to the specific background.
Libraries
Use these libraries to find Image Harmonization models and implementations
iHarmony4
Most implemented papers
Inharmonious Region Localization
bcmi/DIRL
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The advance of image editing techniques allows users to create artistic works, but the manipulated regions may be incompatible with the background.
Deep Image Harmonization
wasidennis/DeepHarmonization
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CVPR 2017
Compositing is one of the most common operations in photo editing.
BargainNet: Background-Guided Domain Translation for Image Harmonization
bcmi/BargainNet-Image-Harmonization
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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.
Improving the Harmony of the Composite Image by Spatial-Separated Attention Module
vinthony/s2am
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Thus, we address the problem of Image Harmonization: Given a spliced image and the mask of the spliced region, we try to harmonize the "style" of the pasted region with the background (non-spliced region).
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2night
bcmi/Image_Harmonization_Datasets
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Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image.
DoveNet: Deep Image Harmonization via Domain Verification
bcmi/Image_Harmonization_Datasets
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CVPR 2020
Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image.
Foreground-aware Semantic Representations for Image Harmonization
saic-vul/image_harmonization
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Image harmonization is an important step in photo editing to achieve visual consistency in composite images by adjusting the appearances of foreground to make it compatible with background.
Generative Hierarchical Features from Synthesizing Images
Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data.
Image Harmonization With Transformer
zhenglab/harmonytransformer
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ICCV 2021
Current solutions mainly adopt an encoder-decoder architecture with convolutional neural network (CNN) to capture the context of composite images, trying to understand what it looks like in the surrounding background near the foreground.
Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization
mengweiren/segmentation-renormalized-harmonization
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Deep networks are now ubiquitous in large-scale multi-center imaging studies.
