Inpainting borehole images using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2301.06152v1
• Date: Sun, 15 Jan 2023 18:15:52 GMT
• Title: Inpainting borehole images using Generative Adversarial Networks
• Authors: Rachid Belmeskine, Abed Benaichouche
• Abstract summary: We propose a GAN-based approach for gap filling in borehole images created by wireline microresistivity imaging tools. The proposed method utilizes a generator, global discriminator, and local discriminator to inpaint the missing regions of the image.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a GAN-based approach for gap filling in borehole images created by wireline microresistivity imaging tools. The proposed method utilizes a generator, global discriminator, and local discriminator to inpaint the missing regions of the image. The generator is based on an auto-encoder architecture with skip-connections, and the loss function used is the Wasserstein GAN loss. Our experiments on a dataset of borehole images demonstrate that the proposed model can effectively deal with large-scale missing pixels and generate realistic completion results. This approach can improve the quantitative evaluation of reservoirs and provide an essential basis for interpreting geological phenomena and reservoir parameters.

Related papers

• Panoramic Image Inpainting With Gated Convolution And Contextual Reconstruction Loss [19.659176149635417]
  We propose a panoramic image inpainting framework that consists of a Face Generator, a Cube Generator, a side branch, and two discriminators. The proposed method is compared with state-of-the-art (SOTA) methods on SUN360 Street View dataset in terms of PSNR and SSIM.
  arXiv  Detail & Related papers  (2024-02-05T11:58:08Z)
• Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold [69.33930972652594]
  Generative Adversarial Networks (GANs) have shown remarkable success in modeling complex data distributions for image-to-image translation. Existing GAN compression methods mainly rely on knowledge distillation or convolutional classifiers' pruning techniques. We propose a new approach by explicitly encouraging the pruned model to preserve the density structure of the original parameter-heavy model on its learned manifold. Our experiments on image translation GAN models, Pix2Pix and CycleGAN, with various benchmark datasets and architectures demonstrate our method's effectiveness.
  arXiv  Detail & Related papers  (2023-12-22T15:43:12Z)
• Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection [86.97062579515833]
  We introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset, comprising samples generated by tft28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance, showcasing a remarkable tft11.6% improvement over existing methods.
  arXiv  Detail & Related papers  (2023-12-16T14:27:06Z)
• Image Deblurring using GAN [0.0]
  This project focuses on the application of Generative Adversarial Network (GAN) in image deblurring. The project defines a GAN model inflow and trains it with GoPRO dataset. The network can obtain sharper pixels in image, achieving an average of 29.3 Peak Signal-to-Noise Ratio (PSNR) and 0.72 Structural Similarity Assessment (SSIM)
  arXiv  Detail & Related papers  (2023-12-15T02:43:30Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• 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)
• Near-filed SAR Image Restoration with Deep Learning Inverse Technique: A Preliminary Study [5.489791364472879]
  Near-field synthetic aperture radar (SAR) provides a high-resolution image of a target's scattering distribution-hot spots. Meanwhile, imaging result suffers inevitable degradation from sidelobes, clutters, and noises. To restore the image, current methods make simplified assumptions; for example, the point spread function (PSF) is spatially consistent, the target consists of sparse point scatters, etc. We reformulate the degradation model into a spatially variable complex-convolution model, where the near-field SAR's system response is considered. A model-based deep learning network is designed to restore the
  arXiv  Detail & Related papers  (2022-11-28T01:28:33Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)
• Image Inpainting Using Wasserstein Generative Adversarial Imputation Network [0.0]
  This paper introduces an image inpainting model based on Wasserstein Generative Adversarial Imputation Network. A universal imputation model is able to handle various scenarios of missingness with sufficient quality.
  arXiv  Detail & Related papers  (2021-06-23T05:55:07Z)
• Pixel-wise Dense Detector for Image Inpainting [34.721991959357425]
  Recent GAN-based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar. We propose a novel detection-based generative framework for image inpainting, which adopts the min-max strategy in an adversarial process. Experiments on multiple public datasets show the superior performance of the proposed framework.
  arXiv  Detail & Related papers  (2020-11-04T13:45:27Z)
• A U-Net Based Discriminator for Generative Adversarial Networks [86.67102929147592]
  We propose an alternative U-Net based discriminator architecture for generative adversarial networks (GANs) The proposed architecture allows to provide detailed per-pixel feedback to the generator while maintaining the global coherence of synthesized images. The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics.
  arXiv  Detail & Related papers  (2020-02-28T11:16:54Z)

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.