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![Figure 1. Examples of Image Inpainting Applications. Image by Jiahui Yu et al. from their paper, DeepFill v2 [13]](https://towardsdatascience.com/wp-content/uploads/2020/11/0XDSquMA8IDDbUrBT.png)
Hello! This post can be regarded as a revision of deep image inpainting for my…
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![Figure 1. Some free-form inpainting results by using DeepFill v2. Note that optional user sketch input is allowed for interactive editing. Image by Jiahui Yu et al. from their paper [1]](https://towardsdatascience.com/wp-content/uploads/2020/11/1Q38k2RnxBkgWSJxzblzbJA.png)
Review: Free-Form Image Inpainting with Gated Convolution
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![Figure 1. Some inpainting results by using the proposed approach (EdgeConnect). Left: Input corrupted/masked images. Middle: Completed edge maps (black: computed edges from valid regions using Canny Edge detector; blue: generated edges for the missing regions using an edge generator) Right: Filled images using the proposed EdgeConnect. Image by Kamyar Nazeri et al. from their paper [1]](https://towardsdatascience.com/wp-content/uploads/2020/11/1l7DbixeejV4eMf3cr3rgpQ.png)
Review – EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
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![Figure 1. Some inpainting results by using Partial Convolutions. Image by Guilin Liu et al. from their paper [1]](https://towardsdatascience.com/wp-content/uploads/2020/11/180hhESN9VWCSZdVoJKPAEA.png)
Review: Image Inpainting for Irregular Holes Using Partial Convolutions
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![Figure 1. Some inpainting results given by the proposed method. Image by Yi Wang et al. from their paper [1]](https://towardsdatascience.com/wp-content/uploads/2020/11/1khibFpGzhICpdvWppiEqAQ.png)
Review: Image Inpainting via Generative Multi-column Convolutional Neural Networks
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![Figure 1. Some examples of inpainting results by the proposed model on natural scene, face, and texture images. Image by Jiahui Yu et al. from their paper [1]](https://towardsdatascience.com/wp-content/uploads/2020/10/1CpWLaX-KOrj7t1klMt64Lw.png)
A Breakthrough in Deep Image Inpainting – Review: Generative Image Inpainting with Contextual…
Deep LearningWelcome back guys! Happy to see you guys:) Last time, we realized that how copy-and-paste…
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![Figure 1. Qualitative comparison of inpainting results by different methods. (a) Input (b) Conventional method (based on copy-and-paste) (c) First GAN-based method, Context Encoder (d) Proposed method. Image by Zhaoyi Yan et al. from their paper [1]](https://towardsdatascience.com/wp-content/uploads/2020/10/135F21D72bzCrMqsCmzkAeA.png)
How ‘Copy-and-Paste’ is embedded in CNNs for Image Inpainting – Review: Shift-Net: Image…
Deep LearningHello everyone:) Welcome back!! Today, we will dive into a more specific deep image inpainting…
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![Figure 1. PatchGAN discriminator. The output is a matrix and each element in the matrix represents a local region in the input image. If the local region is real, we should get 1, else 0. Extracted from [4]](https://towardsdatascience.com/wp-content/uploads/2020/10/1f-cmriSHtg8PKOXR-uWaEg.png)
Revision for Deep Image Inpainting and Review: Patch-Based Image Inpainting with Generative…
Deep LearningWelcome back guys:) Today, I would like to give a revision for deep image inpainting…
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![Figure 1. An example to show the need of generating novel fragments for the task of image inpainting. Extracted and modified from [1]](https://towardsdatascience.com/wp-content/uploads/2020/10/1FoABBYYiYcwNguXG9dVZzw.png)
A Milestone in Deep Image Inpainting – Review: Globally and Locally Consistent Image Completion
Deep LearningWelcome back guys, I hope that the previous posts aroused your curiosity about deep generative…
21 min read
