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

Greatest papers with code

Deep Image Prior

DmitryUlyanov/deep-image-prior 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.

Image Denoising Image Inpainting +3

EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning

aymericdamien/TopDeepLearning 1 Jan 2019

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

Implicit Neural Representations with Periodic Activation Functions

lucidrains/deep-daze 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.

Image Inpainting

SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color

run-youngjoo/SC-FEGAN 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.

Facial Inpainting

Free-Form Image Inpainting with Gated Convolution

JiahuiYu/generative_inpainting ICCV 2019

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

JiahuiYu/generative_inpainting 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.

Image Inpainting

Texture Memory-Augmented Deep Patch-Based Image Inpainting

open-mmlab/mmediting 28 Sep 2020

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

Resolution-robust Large Mask Inpainting with Fourier Convolutions

saic-mdal/lama 15 Sep 2021

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.

Image Inpainting LAMA

Semantic Image Inpainting with Deep Generative Models

bamos/dcgan-completion.tensorflow 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.

Image Inpainting

High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis

leehomyc/Faster-High-Res-Neural-Inpainting 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.

Image Inpainting Image Manipulation