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

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 method divides the inpainting task… 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

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 the algorithm first. A novel… 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

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 (SE) and propose a high-pass… 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

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 reconstruction failure and unintended filling.… 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

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 little distortion and uses it… 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 segments related to the defective… 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 of subjective and objective evaluation… More >