Image Inpainting
276 papers with code • 12 benchmarks • 17 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
These leaderboards are used to track progress in Image Inpainting
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Libraries
Use these libraries to find Image Inpainting models and implementations
ImageNet
CelebA
FFHQ
Places
CelebA-HQ
ApolloScape
Places365
Fashion-Gen
CASIA V2
FVI
Most implemented papers
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
usstdqq/deep-adaptive-sampling-mask
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We propose an XRF image inpainting approach to address the issue of long scanning time, thus speeding up the scanning process while still maintaining the possibility to reconstruct a high quality XRF image.
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
sheqi/GAN_Review
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While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision.
Indoor Depth Completion with Boundary Consistency and Self-Attention
tsunghan-wu/depth-completion
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We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced.
Image2StyleGAN++: How to Edit the Embedded Images?
pacifinapacific/StyleGAN_LatentEditor
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CVPR 2020
We propose Image2StyleGAN++, a flexible image editing framework with many applications.
Image Fine-grained Inpainting
Zheng222/DMFN
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Besides, we devise a geometrical alignment constraint item to compensate for the pixel-based distance between prediction features and ground-truth ones.
Accelerated MRI with Un-trained Neural Networks
Convolutional Neural Networks (CNNs) are highly effective for image reconstruction problems.
AIM 2020 Challenge on Image Extreme Inpainting
This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting.
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
andreas128/RePaint
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CVPR 2022
In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks.
Feature Refinement to Improve High Resolution Image Inpainting
geomagical/lama-with-refiner
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In this paper, we address the problem of degradation in inpainting quality of neural networks operating at high resolutions.
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
wyhuai/ddnm
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Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators.
