ECNFP: Edge-constrained network using a feature pyramid for image inpainting

Wei, Zitai; Min, Weidong; Wang, Qi; Liu, Qian; Zhao, Haoyu

doi:10.1016/j.eswa.2022.118070

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…The authors of [96] proposed a texture generator using appearance flow for the second stage after generating edge structural information in the first stage. Wei et al [97] obtained structural information, then, in the second stage, employed a feature pyramid module to merge multiscale features, obtaining low-, medium-, and high-level semantic information. This enhances the color and texture visual effects of the restored area.…”

Section: Multistage Network Structurementioning

confidence: 99%

A Review of Image Inpainting Methods Based on Deep Learning

Xu,

Zhang,

Chen

et al. 2023

Applied Sciences

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Image Inpainting is an age-old image processing problem, with people from different eras attempting to solve it using various methods. Traditional image inpainting algorithms have the ability to repair minor damage such as scratches and wear. However, with the rapid development of deep learning in the field of computer vision in recent years, coupled with abundant computing resources, methods based on deep learning have increasingly highlighted their advantages in semantic feature extraction, image transformation, and image generation. As such, image inpainting algorithms based on deep learning have become the mainstream in this domain.In this article, we first provide a comprehensive review of some classic deep-learning-based methods in the image inpainting field. Then, we categorize these methods based on component optimization, network structure design optimization, and training method optimization, discussing the advantages and disadvantages of each approach. A comparison is also made based on public datasets and evaluation metrics in image inpainting. Furthermore, the article delves into the applications of current image inpainting technologies, categorizing them into three major scenarios: object removal, general image repair, and facial inpainting. Finally, current challenges and prospective developments in the field of image inpainting are discussed.

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Section: Multistage Network Structurementioning

confidence: 99%

A Review of Image Inpainting Methods Based on Deep Learning

Xu,

Zhang,

Chen

et al. 2023

Applied Sciences

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“…Indeed these methods show high performances in recovering corrupted data of small holes in low-resolution images, as well as regular structure shapes [14]. However, inpainting of complex shaped textures, large-sized irregular holes and high-resolution images are the main hotspots that deserve special attention in image inpainting techniques research [14,15]. To overcome this issue, the current paper introduces a new approach for image inpainting in blind fragile image watermarking.…”

Section: Introductionmentioning

confidence: 99%

AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models

Daoui,

Yamni,

Altameem

et al. 2023

Sensors

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Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images.

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