Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels

• URL: http://arxiv.org/abs/2112.09262v1
• Date: Fri, 17 Dec 2021 00:10:34 GMT
• Title: Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels
• Authors: Mohammad H. Givkashi, Mahshid Hadipour, Arezoo PariZanganeh, Zahra Nabizadeh, Nader Karimi, Shadrokh Samavi
• Abstract summary: In this paper, we propose a network for image inpainting. This network, similar to U-Net, extracts various features from images, leading to better results. We improved the final results by replacing the damaged pixels with the recovered pixels of the output images.
• Score: 9.527576103168984
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Image inpainting is an effective method to enhance distorted digital images. Different inpainting methods use the information of neighboring pixels to predict the value of missing pixels. Recently deep neural networks have been used to learn structural and semantic details of images for inpainting purposes. In this paper, we propose a network for image inpainting. This network, similar to U-Net, extracts various features from images, leading to better results. We improved the final results by replacing the damaged pixels with the recovered pixels of the output images. Our experimental results show that this method produces high-quality results compare to the traditional methods.

Related papers

• Pixel-Inconsistency Modeling for Image Manipulation Localization [63.54342601757723]
  Digital image forensics plays a crucial role in image authentication and manipulation localization. This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
  arXiv  Detail & Related papers  (2023-09-30T02:54:51Z)
• SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting [26.309834304515544]
  We introduce a challenging image restoration task, referred to as SuperInpaint. This task aims to reconstruct missing regions in low-resolution images and generate completed images with arbitrarily higher resolutions. We propose the detail-enhanced attentional implicit representation that can achieve SuperInpaint with a single model.
  arXiv  Detail & Related papers  (2023-07-26T20:28:58Z)
• Perceptual Artifacts Localization for Inpainting [60.5659086595901]
  We propose a new learning task of automatic segmentation of inpainting perceptual artifacts. We train advanced segmentation networks on a dataset to reliably localize inpainting artifacts within inpainted images. We also propose a new evaluation metric called Perceptual Artifact Ratio (PAR), which is the ratio of objectionable inpainted regions to the entire inpainted area.
  arXiv  Detail & Related papers  (2022-08-05T18:50:51Z)
• In&Out : Diverse Image Outpainting via GAN Inversion [89.84841983778672]
  Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image.
  arXiv  Detail & Related papers  (2021-04-01T17:59:10Z)
• Restore from Restored: Single-image Inpainting [9.699531255678856]
  We present a novel and efficient self-supervised fine-tuning algorithm for inpainting networks. We upgrade the parameters of the pretrained networks by utilizing existing self-similar patches within the given input image. We achieve state-of-the-art inpainting results on publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2021-02-16T10:59:28Z)
• R-MNet: A Perceptual Adversarial Network for Image Inpainting [5.471225956329675]
  We propose a Wasserstein GAN combined with a new reverse mask operator, namely Reverse Masking Network (R-MNet), a perceptual adversarial network for image inpainting. We show that our method is able to generalize to high-resolution inpainting task, and further show more realistic outputs that are plausible to the human visual system.
  arXiv  Detail & Related papers  (2020-08-11T10:58:10Z)
• Deep Photo Cropper and Enhancer [65.11910918427296]
  We propose a new type of image enhancement problem: to crop an image which is embedded within a photo. We split our proposed approach into two deep networks: deep photo cropper and deep image enhancer. In the photo cropper network, we employ a spatial transformer to extract the embedded image. In the photo enhancer, we employ super-resolution to increase the number of pixels in the embedded image.
  arXiv  Detail & Related papers  (2020-08-03T03:50:20Z)
• Swapping Autoencoder for Deep Image Manipulation [94.33114146172606]
  We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation. The key idea is to encode an image with two independent components and enforce that any swapped combination maps to a realistic image. Experiments on multiple datasets show that our model produces better results and is substantially more efficient compared to recent generative models.
  arXiv  Detail & Related papers  (2020-07-01T17:59:57Z)
• High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling [122.06593036862611]
  Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. We propose an iterative inpainting method with a feedback mechanism. Experiments show that our method significantly outperforms existing methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2020-05-24T13:23:45Z)
• Large Hole Image Inpainting With Compress-Decompression Network [0.0]
  Existing methods propose convolutional neural networks to repair corrupted images. We study the existing approaches and propose a new network, the compression-decompression network. We construct the compression network with the residual network and propose a similar texture selection algorithm to extend the image.
  arXiv  Detail & Related papers  (2020-02-01T12:39:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.