Overview of Image Inpainting Techniques: A Survey

Shah, Riya; Gautam, Anjali; Singh, Satish Kumar

doi:10.1109/tensymp54529.2022.9864513

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Training a GAN on images with and without the object of interest can be used to fill in the empty space after object removal. The generator network learns from the surrounding context to generate plausible content for the missing region, whereas the discriminator network learns from the surrounding context to differentiate between generated and actual images [48].…”

Section: Inpainting Techniques To Fill In Missing Space After Object ...mentioning

confidence: 99%

From Augmentation to Inpainting: Improving Visual SLAM With Signal Enhancement Techniques and GAN-Based Image Inpainting

Theodorou,

Velisavljevic,

Dyo

et al. 2024

IEEE Access

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This paper undertakes a comprehensive investigation that surpasses the conventional examination of signal enhancement techniques and their effects on visual Simultaneous Localization and Mapping (vSLAM) performance across diverse scenarios. Going beyond the conventional scope, the study extends its focus towards the seamless integration of signal enhancement techniques, aiming to achieve a substantial enhancement in the overall vSLAM performance. The research not only delves into the assessment of existing methods but also actively contributes to the field by proposing innovative denoising techniques that can play a pivotal role in refining the accuracy and reliability of vSLAM systems. This multifaceted approach encompasses a thorough exploration of the intricate relationships between signal enhancement, denoising strategies, their cumulative impact on the performance of vSLAM in real-world applications and the innovative use of Generative Adversarial Networks (GANs) for image inpainting. The GANs effectively fill in missing spaces following object detection and removal, presenting a novel state-of-theart approach that significantly enhances overall accuracy and execution speed of vSLAM. This paper aims to contribute to the advancement of vSLAM algorithms in real-world scenarios, demonstrating improved accuracy, robustness, and computational efficiency through the amalgamation of signal enhancement and advanced denoising techniques.INDEX TERMS signal enhancement, denoising techniques, visual SLAM, object detection, simultaneous localization and mapping(SLAM), object detection and generative adversarial network (GAN).

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Section: Inpainting Techniques To Fill In Missing Space After Object ...mentioning

confidence: 99%

From Augmentation to Inpainting: Improving Visual SLAM With Signal Enhancement Techniques and GAN-Based Image Inpainting

Theodorou,

Velisavljevic,

Dyo

et al. 2024

IEEE Access

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“…Image inpainting is a long-lasting research question in computer vision, aiming at completing a degraded image naturally and coherently (Xiang et al, 2022;Shah et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

MRM-Net: Towards Deep Multi-Resolution Mutual Learning for Progressive Image Inpainting

Zhang¹,

Zhang²,

Zhang³

et al. 2022

Preprint

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<p>Deep image inpainting methods have improved the inpainting performance greatly due to the powerful representation ability of deep learning. However, current deep inpainting models still tend to produce unreasonable structures and blurry textures due to the ill-posed properties of the task, i.e., image inpainting is still a challenging topic. In this paper, we therefore propose a novel deep multi-resolution mutual learning (DMRML) strategy for progressive inpainting, which can fully explore the information from various resolutions. Specifically, we design a new image inpainting network, termed multi-resolution mutual network (MRM-Net), which takes the damaged images of various resolutions as input, then excavates and exploits the correlation among different resolutions to guide the image inpainting process. Technically, the setting of MRM-Net designs two new modules called multi-resolution information interaction (MRII) and adaptive content enhancement (ACE). MRII aims at discovering the correlation of multiple resolutions and exchanging information, and ACE focuses on enhancing the contents using the interacted features. We also present an memory preservation mechanism (MPM) to prevent from the information loss with the increasing layers. Extensive experiments on Paris Street View, Places2 and CelebA-HQ datasets demonstrate that our proposed MRM-Net can effectively recover the textures and structures, and performs favorably against other state-of-the-art methods. </p>

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From past to present: A tertiary investigation of twenty-four years of image inpainting

Barcelos,

Rabelo,

Bernardini

et al. 2024

Computers & Graphics

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Overview of Image Inpainting Techniques: A Survey

Cited by 5 publications

References 35 publications

From Augmentation to Inpainting: Improving Visual SLAM With Signal Enhancement Techniques and GAN-Based Image Inpainting

From Augmentation to Inpainting: Improving Visual SLAM With Signal Enhancement Techniques and GAN-Based Image Inpainting

MRM-Net: Towards Deep Multi-Resolution Mutual Learning for Progressive Image Inpainting

From past to present: A tertiary investigation of twenty-four years of image inpainting

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