Dengyong Zhang^1,2, Yuting Zhao^1,2, Feng Li^1,2, Arun Kumar Sangaiah^3,4,*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 115-133, 2024, DOI:10.32604/iasc.2024.040847 - 21 May 2024

Abstract Image inpainting is a kind of use known area of information technology to repair the loss or damage to the area. Image feature extraction is the core of image restoration. Getting enough space for information and a larger receptive field is very important to realize high-precision image inpainting. However, in the process of feature extraction, it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same time. In order to obtain more spatial information and a larger receptive field at the same time, we put forward… More >

Jinxian Bai, Yao Fan^*, Zhiwei Zhao, Lizhi Zheng

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 999-1025, 2024, DOI:10.32604/cmc.2023.044612 - 30 January 2024

Abstract Recently, deep learning-based image inpainting methods have made great strides in reconstructing damaged regions. However, these methods often struggle to produce satisfactory results when dealing with missing images with large holes, leading to distortions in the structure and blurring of textures. To address these problems, we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms. The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details. This… More >

Haitao Hu¹, Hongmei Ma², Shuli Mei^1,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3813-3832, 2023, DOI:10.32604/cmc.2023.041416 - 08 October 2023

Abstract Biological slices are an effective tool for studying the physiological structure and evolution mechanism of biological systems. However, due to the complexity of preparation technology and the presence of many uncontrollable factors during the preparation processing, leads to problems such as difficulty in preparing slice images and breakage of slice images. Therefore, we proposed a biological slice image small-scale corruption inpainting algorithm with interpretability based on multi-layer deep sparse representation, achieving the high-fidelity reconstruction of slice images. We further discussed the relationship between deep convolutional neural networks and sparse representation, ensuring the high-fidelity characteristic of… More >

Can Xiao^1,2, Feng Li^1,2,*, Dengyong Zhang^1,2, Pu Huang^1,2, Xiangling Ding³, Victor S. Sheng⁴

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1145-1154, 2022, DOI:10.32604/csse.2022.027249 - 09 May 2022

Abstract Image inpainting based on deep learning has been greatly improved. The original purpose of image inpainting was to repair some broken photos, such as inpainting artifacts. However, it may also be used for malicious operations, such as destroying evidence. Therefore, detection and localization of image inpainting operations are essential. Recent research shows that high-pass filtering full convolutional network (HPFCN) is applied to image inpainting detection and achieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, we introduce the squeezed excitation blocks More >

Chung-Il Kim¹, Jehyeok Rew², Yongjang Cho², Eenjun Hwang^2,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3447-3463, 2021, DOI:10.32604/cmc.2021.017633 - 06 May 2021

Abstract Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas. Although its performance has been improved significantly using diverse convolutional neural network (CNN)-based models, these models have difficulty filling in some erased areas due to the kernel size of the CNN. If the kernel size is too narrow for the blank area, the models cannot consider the entire surrounding area, only partial areas or none at all. This issue leads to typical problems of inpainting, such as pixel More >

Seo Woo Han, Doug Young Suh^*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 213-228, 2021, DOI:10.32604/cmc.2020.012223 - 30 October 2020

Abstract Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively More >

Haitong Zhao¹, Yi Li¹, Shouhua Luo^1,*

Molecular & Cellular Biomechanics, Vol.16, No.4, pp. 265-273, 2019, DOI:10.32604/mcb.2019.07381

Abstract The ring artifacts introduced by the defective pixels with non-linear responses in the high-resolution detector, have a great impact on subsequent processing and quantitative analysis of the reconstructed images. In this paper, a multistep method is proposed to suppress the ring artifacts of micro CT images, which firstly locates the positions of the defective pixels in the sinogram, and then corrects the corresponding value in the projections. Since the defective pixels always appear as vertical stripes in the sinogram, a horizontal curve is derived by summing the pixel values along vertical direction, thus the abrupt… More >

Wei Chen^{1, 2}, Tingzhu Sun^{1, 2}, Fangming Bi^{1, 2, *}, Tongfeng Sun^{1, 2}, Chaogang Tang^{1, 2}, Biruk Assefa^{1, 3}

Journal of New Media, Vol.1, No.1, pp. 35-44, 2019, DOI:10.32604/jnm.2019.05803

Abstract Image restoration is an image processing technology with great practical value in the field of computer vision. It is a computer technology that estimates the image information of the damaged area according to the residual image information of the damaged image and carries out automatic repair. This article firstly classify and summarize image restoration algorithms, and describe recent advances in the research respectively from three aspects including image restoration based on partial differential equation, based on the texture of image restoration and based on deep learning, then make the brief analysis of digital image restoration More >