Image Inpainting
137 papers with code • 7 benchmarks • 10 datasets
Image Inpainting is a task of reconstructing missing regions in an image. It is an important problem in computer vision and an essential functionality in many imaging and graphics applications, e.g. object removal, image restoration, manipulation, re-targeting, compositing, and image-based rendering.
Source: High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling
Image source: High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling
Benchmarks
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Datasets
ImageNet
Places
FFHQ
CelebA-HQ
ApolloScape
Fashion-Gen
CASIA V2
FVI
FDF
Apolloscape Inpainting
Greatest papers with code
Deep Image Prior
DmitryUlyanov/deep-image-prior
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CVPR 2018
In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
aymericdamien/TopDeepLearning
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The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori.
Implicit Neural Representations with Periodic Activation Functions
lucidrains/deep-daze
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NeurIPS 2020
However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations.
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
run-youngjoo/SC-FEGAN
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ICCV 2019
We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input.
Free-Form Image Inpainting with Gated Convolution
JiahuiYu/generative_inpainting
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ICCV 2019
We present a generative image inpainting system to complete images with free-form mask and guidance.
Ranked #3 on
Image Inpainting
on Places2 val
Generative Image Inpainting with Contextual Attention
JiahuiYu/generative_inpainting
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CVPR 2018
Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions.
Texture Memory-Augmented Deep Patch-Based Image Inpainting
By bringing together the best of both paradigms, we propose a new deep inpainting framework where texture generation is guided by a texture memory of patch samples extracted from unmasked regions.
Resolution-robust Large Mask Inpainting with Fourier Convolutions
saic-mdal/lama
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We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function.
Semantic Image Inpainting with Deep Generative Models
bamos/dcgan-completion.tensorflow
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CVPR 2017
In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data.
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
leehomyc/Faster-High-Res-Neural-Inpainting
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CVPR 2017
Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal.