Image Inpainting Edit Task
Computer Vision • Image Generation
About
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
Benchmarks
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Subtasks
Datasets
Greatest papers with code
Deep Image Prior
CVPR 2018
• DmitryUlyanov/deep-image-prior
• 
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.
IMAGE DENOISING IMAGE INPAINTING IMAGE RESTORATION JPEG COMPRESSION ARTIFACT REDUCTION SUPER-RESOLUTION
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
• aymericdamien/TopDeepLearning
• 
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.
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
ICCV 2019
• run-youngjoo/SC-FEGAN
•
We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input.
Implicit Neural Representations with Periodic Activation Functions
NeurIPS 2020
• lucidrains/deep-daze
•
We propose to leverage periodic activation functions for implicit neural representations and demonstrate that these networks, dubbed sinusoidal representation networks or Sirens, are ideally suited for representing complex natural signals and their derivatives.
Free-Form Image Inpainting with Gated Convolution
ICCV 2019
• JiahuiYu/generative_inpainting
•
We present a generative image inpainting system to complete images with free-form mask and guidance.
Ranked #1 on
Image Inpainting
on Places2 val
Generative Image Inpainting with Contextual Attention
CVPR 2018
• JiahuiYu/generative_inpainting
•
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.
Ranked #1 on
Image Inpainting
on Places2 val
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.
Semantic Image Inpainting with Deep Generative Models
CVPR 2017
• bamos/dcgan-completion.tensorflow
•
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
CVPR 2017
• leehomyc/Faster-High-Res-Neural-Inpainting
• 
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.
Image Inpainting with Learnable Feature Imputation
We propose (layer-wise) feature imputation of the missing input values to a convolution.