Browse > Computer Vision > Image Inpainting

Image Inpainting

60 papers with code · Computer Vision

Leaderboards

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 GENERATION 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

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

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 Inpainting for Irregular Holes Using Partial Convolutions

ECCV 2018 MathiasGruber/PConv-Keras

Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value).

IMAGE INPAINTING

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

CVPR 2017 Evolving-AI-Lab/ppgn

PPGNs are composed of 1) a generator network G that is capable of drawing a wide range of image types and 2) a replaceable "condition" network C that tells the generator what to draw.

IMAGE CAPTIONING IMAGE INPAINTING

"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors

Computer Vision Foundation 2018 yossigandelsman/DoubleDIP

It was shown [Ulyanov et al] that the structure of a single DIP generator network is sufficient to capture the low-level statistics of a single image.

IMAGE DEHAZING IMAGE INPAINTING JPEG ARTIFACT REMOVAL SEMANTIC SEGMENTATION TRANSPARENCY SEPARATION