Inpaint 6.0 key
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Simply speaking, two advanced concepts namely residual learning and PatchGAN were embedded in GLCIC to further boost its inpainting performance. Patch-based Image Inpainting with GANs can be regarded as a variant of GLCIC. Image by Ugur Demir and Gozde Unal from their paper The proposed Generative ResNet architecture and PGGAN discriminator. Patch-based Image Inpainting with GANs (A Variant of GLCIC, 2018)įigure 6. If you are interested in this paper, please visit my previous post for more details. Note that many later inpainting papers follow this multi-scale discriminator design. With both the global and local discriminators, the filled image would have better global and local consistency. A global discriminator looks at the whole image while a local discriminator looks at the filled centre hole.
Inpaint 6.0 key generator#
By using Dilated convolutions, the network is able to understand the context of an image without employing expensive fully connected layers and hence it can handle images of different sizes.Īpart from the fully convolution network with dilated convolutions, two discriminators at two scales were also trained together with the generator network. Globally and Locally Consistent Image Completion (GLCIC, 2017) is a milestone in deep image inpainting as it defines the Fully Convolution Network with Dilated Convolutions for deep image inpainting and actually this is a typical network structure for deep image inpainting. Overview of the proposed model which consists of a completion network (Generator network), a global discriminator, and a local discriminator. GLCIC (A Milestone in Deep Image Inpainting, 2017)įigure 5. To know more about this paper, you may refer to my previous post.
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Actually, texture loss is related to perceptual loss and style loss that are widely used in many image generation tasks such as neural style transfer. L1 loss) and the standard Adversarial loss, the concept of texture loss proposed in this paper plays an important role in later inpainting papers. texture network here) is responsible for the refinement of the filled parts.Īpart from the typical pixel-wise reconstruction loss (i.e. CE here) is responsible for the reconstruction/prediction of the missing parts while the second network (i.e. I would say that this work is an early version of the two-stage coarse-to-fine network structure.
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We would like to transfer the style of the most similar valid pixels to the generated pixels to enhance the local texture details. The idea of the texture network is from the task of style transfer. The authors of this paper employed a modified CE to predict the missing parts in an image and a texture network to decorate the prediction about the missing parts to improve the visual quality of the filled images.
Inpaint 6.0 key Patch#
Multi-Scale Neural Patch Synthesis (MSNPS, 2016) can be regarded as an enhanced version of CE. Overview of the content network (modified CE ) and the texture network (VGG-19). Let’s Go:) Context Encoder (1st GAN-based inpainting, 2016)įigure 4. I am sure that you can understand other inpainting papers/works after you realizing these 10 approaches. We will focus on 10 famous deep learning-based inpainting approaches in this post. Thanks to deep learning-based approaches and the era of Big Data, we can now have data-driven deep learning-based image inpainting approaches that can generate the missing pixels in an image with good global consistency and local fine textures. Its generalization and efficiency still have plenty of room for improvement. However, the search algorithm could be time-consuming and it involves hand-crafted distance measure metrics. The core idea is to first search for the most similar image patches from the remaining pixels of the image itself or a large dataset with millions of images, then directly paste the patches on the missing parts. To fill in an image with some missing parts, the simplest way is to copy-and-paste. Note that we can directly copy the valid pixels and paste them on the filled image at their corresponding locations. Given a corrupted/masked input image as shown in Figure 2 (left), we usually define i) invalid/missing/hole pixels as the pixels located at the region(s) to be filled ii) valid/remaining/ground truth pixels as the pixels we can use to help filling in the missing pixels. Image by the author extracted from his github page An example of a masked input image (left) and a completed image (right).