Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels

Givkashi, Mohammad Hossein; Hadipour, Mahshid; PariZanganeh, Arezoo; Nabizadeh, Zahra; Karimi, Nader; Samavi, Shadrokh

doi:10.1109/icee55646.2022.9827427

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…In [30], by Givkashi, M. H., Hadipour, M., et al, a method for image restoration using an autoencoder and controlled selection of predicted pixels is presented. In this work, the authors propose to use a network similar to U-net; due to the neural network, damaged pixels are replaced with restored pixels of the output images.…”

Section: Related Workmentioning

confidence: 99%

Visible watermarks inpainting using deep learning for multimedia application

Gapon,

Yadrets,

Voronin

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

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In recent decades, with the development of digital technologies and the spread of the Internet, photography has become an integral part of our daily lives. Often, images with objects superimposed in text, symbols, or drawings. These superimposed objects can serve a variety of purposes. Adding text or graphics can highlight specific details in an image or convey important information, such as the event's date, location, or specifics. Adding graphic elements may be used commercially to promote products, services, or a brand. Another area of application of such objects is the field of intellectual property protection or verification of the authenticity of a digital image. The problem with such images is that objects can completely overlap the image, leading to a distortion of the perceived information. In this article, we propose an effective convolutional autoencoder model for removing watermarking and restoring an image's usable part. This network can perfectly remove the embedded, visible watermark, restoring the image. Experimental results demonstrate the effectiveness and superiority of our scheme.

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Section: Related Workmentioning

confidence: 99%

Visible watermarks inpainting using deep learning for multimedia application

Gapon,

Yadrets,

Voronin

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

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“…With the emergence of convolution neural networks, some scholars have used a neural network to detect and repair scratches. For example, references [11,12] use a U-net to detect scratch damage in old movies, but the actual test results can only detect the parts with simple background information in the main scratches. Iizuka et al Put forward DeepRemaster [13] , which uses time convolution to restore old movie damage and repair video artifacts, but has a poor perception of scratch damage.…”

Section: Fig1 Scratches In Old Moviesmentioning

confidence: 99%

Research on Scratch Detection of Old Movies

Liu

Wang

2022

Preprint

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Scratches are a continuous structural injury in old movies. Scratch pixel level segmentation algorithm still uses the general image segmentation algorithm, and the detection results are poor. According to the characteristics of scratches, a scratch model based on the Gaussian probability density function is proposed, a local adaptive one-dimensional Gaussian weighted segmentation algorithm is designed, and an invalid region filtering mechanism is introduced. Compared with several groups of experimental results, the local one-dimensional Gaussian weighted segmentation algorithm proposed in this paper can well preserve the integrity and continuity of scratches, and effectively suppress edge information, and the invalid region filtering mechanism can effectively reduce the false detection of scratches. Adding scratch trajectory can effectively reduce the missed detection rate of scratches, and the stability of the traditional algorithm using Kalman to predict scratch trajectory is poor. According to the similarity of multiple scratch trajectories, a calculation method of scratch trajectories based on U-net is designed. Experimental results show that this method can calculate scratch trajectory stably and effectively.

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