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

1 Jan 2019aymericdamien/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.

IMAGE INPAINTING

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.

FACIAL INPAINTING

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.

IMAGE INPAINTING

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.

FEATURE SELECTION IMAGE INPAINTING

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.

IMAGE INPAINTING

Texture Memory-Augmented Deep Patch-Based Image Inpainting

28 Sep 2020open-mmlab/mmediting

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.

IMAGE INPAINTING TEXTURE SYNTHESIS

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.

IMAGE INPAINTING

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 IMAGE MANIPULATION

Image Inpainting with Learnable Feature Imputation

2 Nov 2020hukkelas/DeepPrivacy

We propose (layer-wise) feature imputation of the missing input values to a convolution.

IMAGE INPAINTING IMPUTATION