Image Inpainting
279 papers with code • 12 benchmarks • 17 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
Libraries
Use these libraries to find Image Inpainting models and implementationsMost implemented papers
Semantic Image Inpainting with Deep Generative Models
In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data.
Resolution-robust Large Mask Inpainting with Fourier Convolutions
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.
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
Since convolutional layers of the neural network only need to operate on low-resolution inputs and outputs, the cost of memory and computing power is thus well suppressed.
Deep Fusion Network for Image Completion
The fusion block not only provides a smooth fusion between restored and existing content, but also provides an attention map to make network focus more on the unknown pixels.
Joint learning of variational representations and solvers for inverse problems with partially-observed data
The variational cost and the gradient-based solver are both stated as neural networks using automatic differentiation for the latter.
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input.
Indoor Depth Completion with Boundary Consistency and Self-Attention
We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced.
High-Fidelity Pluralistic Image Completion with Transformers
Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity.
Image Inpainting via Conditional Texture and Structure Dual Generation
Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors.
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
Our approach models an image as a composition of label and latent attributes in a probabilistic model.