Restore from Restored: Single-image Inpainting

• URL: http://arxiv.org/abs/2102.08078v1
• Date: Tue, 16 Feb 2021 10:59:28 GMT
• Title: Restore from Restored: Single-image Inpainting
• Authors: Eun Hye Lee, Jeong Mu Kim, Ji Su Kim, Tae Hyun Kim
• Abstract summary: 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.
• Score: 9.699531255678856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent image inpainting methods show promising results due to the power of deep learning, which can explore external information available from a large training dataset. However, many state-of-the-art inpainting networks are still limited in exploiting internal information available in the given input image at test time. To mitigate this problem, we present a novel and efficient self-supervised fine-tuning algorithm that can adapt the parameters of fully pretrained inpainting networks without using ground-truth clean image in this work. We upgrade the parameters of the pretrained networks by utilizing existing self-similar patches within the given input image without changing network architectures. Qualitative and quantitative experimental results demonstrate the superiority of the proposed algorithm and we achieve state-of-the-art inpainting results on publicly available numerous benchmark datasets.

Related papers

• Dense Feature Interaction Network for Image Inpainting Localization [28.028361409524457]
  Inpainting can be used to conceal or alter image contents in malicious manipulation of images. Existing methods mostly rely on a basic encoder-decoder structure, which often results in a high number of false positives. In this paper, we describe a new method for inpainting detection based on a Dense Feature Interaction Network (DeFI-Net)
  arXiv  Detail & Related papers  (2024-08-05T02:35:13Z)
• GRIG: Few-Shot Generative Residual Image Inpainting [27.252855062283825]
  We present a novel few-shot generative residual image inpainting method that produces high-quality inpainting results. The core idea is to propose an iterative residual reasoning method that incorporates Convolutional Neural Networks (CNNs) for feature extraction. We also propose a novel forgery-patch adversarial training strategy to create faithful textures and detailed appearances.
  arXiv  Detail & Related papers  (2023-04-24T12:19:06Z)
• Visual Prompting via Image Inpainting [104.98602202198668]
  Inspired by prompting in NLP, this paper investigates visual prompting: given input-output image example(s) of a new task at test time and a new input image. We apply visual prompting to pretrained models and demonstrate results on various downstream image-to-image tasks.
  arXiv  Detail & Related papers  (2022-09-01T17:59:33Z)
• 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)
• Learning Prior Feature and Attention Enhanced Image Inpainting [63.21231753407192]
  This paper incorporates the pre-training based Masked AutoEncoder (MAE) into the inpainting model. We propose to use attention priors from MAE to make the inpainting model learn more long-distance dependencies between masked and unmasked regions.
  arXiv  Detail & Related papers  (2022-08-03T04:32:53Z)
• Restore from Restored: Single-image Inpainting [9.699531255678856]
  We present a novel and efficient self-supervised fine-tuning algorithm for inpainting networks. We update the parameters of the pre-trained inpainting networks by utilizing existing self-similar patches. We achieve state-of-the-art inpainting results on publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-25T11:38:51Z)
• Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting [42.189768203036394]
  We make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well. Our approach outperforms existing detection methods by a large margin and generalizes well to unseen deep inpainting techniques.
  arXiv  Detail & Related papers  (2021-06-03T01:29:29Z)
• Image inpainting using frequency domain priors [35.54138025375951]
  We present a novel image inpainting technique using frequency domain information. We evaluate our proposed method on the publicly available datasets CelebA, Paris Streetview, and DTD texture dataset.
  arXiv  Detail & Related papers  (2020-12-03T11:08:13Z)
• Free-Form Image Inpainting via Contrastive Attention Network [64.05544199212831]
  In image inpainting tasks, masks with any shapes can appear anywhere in images which form complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. We propose a self-supervised Siamese inference network to improve the robustness and generalization.
  arXiv  Detail & Related papers  (2020-10-29T14:46:05Z)
• 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)
• Very Long Natural Scenery Image Prediction by Outpainting [96.8509015981031]
  Outpainting receives less attention due to two challenges in it. First challenge is how to keep the spatial and content consistency between generated images and original input. Second challenge is how to maintain high quality in generated results.
  arXiv  Detail & Related papers  (2019-12-29T16:29:01Z)

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.