Image Inpainting
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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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
• 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.
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.
SOTA for 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.
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 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).
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.
"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