An Automatic Image Inpainting Algorithm Based on FCM by Jiansheng Liu, Hui Liu, Shangping Qiao, Guangxue Yue

“…There are many existing image inpainting algorithms in which the repaired area should be manually determined by users. …”
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Image Inpainting of Portraits Artwork Design and Implementation by Zhang Hongting

“…This paper simulates artificial damage to classic portrait paintings in the Art Portraits dataset by adding center masks during data preprocessing and then implements the image inpainting task. During the training phase, the Denoising Diffusion Probabilistic Model (DDPM) is fine-tuned by progressively adding noise to the center-masked images in the noising stage, followed by denoising in the denoising stage to generate images. …”
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Region inpainting algorithm of mouth-muffles for facial recognition by Yue LI, Yaguan QIAN, Xiaohui GUAN, Wei LI, Bin WANG, Zhaoquan GU

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Error-Mask-Adaptive Dynamic Filtering for Image Inpainting by Keunsoo Ko, Seunggyun Woo, Chang-Su Kim

Subjects: “…Image inpainting…”
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An image inpainting method based on weighted priority and classification matching by Yan CAO, Wei JIN, Randi FU

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An Adaptive Image Inpainting Method Based on Continued Fractions Interpolation by Lei He, Yan Xing, Kangxiong Xia, Jieqing Tan

“…In view of the drawback of most image inpainting algorithms by which texture was not prominent, an adaptive inpainting algorithm based on continued fractions was proposed in this paper. …”
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Image inpainting forensics method based on dual branch network by Dengyong ZHANG, Huang WEN, Feng LI, Peng CAO, Lingyun XIANG, Gaobo YANG, Xiangling DING

“…Image inpainting is a technique that uses information from known areas of an image to repair missing or damaged areas of the image.Image editing software based on it has made it easy to edit and modify the content of digital images without any specialized foundation.When image inpainting techniques are used to maliciously remove the content of an image, it will cause confidence crisis on the real image.Current researches in image inpainting forensics can only effectively detect a certain type of image inpainting.To address this problem, a passive forensic method for image inpainting was proposed, which is based on a two-branch network.The high-pass filtered convolutional network in the dual branch first used a set of high-pass filters to attenuate the low-frequency components in the image.Then features were extracted using four residual blocks, and two transposed convolutions were performed with 4x up-sampling to zoom in on the feature map.And thereafter a 5×5 convolution was used to attenuate the tessellation artifacts from the transposed convolutions to generate a discriminative feature map on the high-frequency components of the image.The dual-attention feature fusion branch in the dual branch first added a local binary pattern feature map to the image using a preprocessing block.Then the dual-attention convolution block was used to adaptively integrate the image’s local features and global dependencies to capture the differences in content and texture between the inpainted and pristine regions of the image.Additionally, the features extracted from the dual-attention convolution block were fused, and the feature maps were up-sampled identically to generate the discriminative image content and texture on the feature maps.The extensive experimental results show the proposed method improved the F1 score by 2.05% and the Intersection over Union(IoU) by 3.53% for the exemplar-based method and by 1.06% and 1.22% for the deep-learning-based method in detecting the inpainted region of the removed object.Visualization of the results shows that the edges of the removed objects can be accurately located on the detected inpainted area.…”
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Pixel-Level Non-Local Method-Based Depth Image Inpainting by Xiaoya Dai, Yingkun Hou, Shuqi Zhang, Hao Hou, Bin Feng, Tao Lin, Mengyu Liu

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Efficient Image Inpainting for Handwritten Text Removal Using CycleGAN Framework by Somanka Maiti, Shabari Nath Panuganti, Gaurav Bhatnagar, Jonathan Wu

Subjects: “…image inpainting…”
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Self-Supervised Deep Hyperspectral Inpainting with Plug-and-Play and Deep Image Prior Models by Shuo Li, Mehrdad Yaghoobi

Subjects: “…hyperspectral image inpainting…”
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Contour Detection and Completion for Inpainting and Segmentation Based on Topological Gradient and Fast Marching Algorithms by Didier Auroux, Laurent D. Cohen, Mohamed Masmoudi

“…Several image processing problems (e.g., inpainting and segmentation) require continuous contours. …”
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Efficient guided inpainting of larger hole missing images based on hierarchical decoding network by Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He

“…Abstract When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. …”
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Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme by Shuli Ma, Youchen Fan, Shengliang Fang, Weichao Yang, Li Li

Subjects: “…image inpainting…”
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Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks by Haoming Zhang, Yue Qi, Xiaoting Xue, Yahui Nan

“…Therefore, we propose a basic framework for image denoising and inpainting of stone inscriptions based on deep learning methods. …”
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Retracted: Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks by Discrete Dynamics in Nature and Society

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Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting by Yujun Cao, Yubiao Yue, Xiaoming Ma, Di Liu, Rongkai Ni, Haihua Liang, Zhenzhang Li

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Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method by Jian-Jia Su, Chung-Hao Tien

“…In this paper, we proposed a two-step algorithm that traditional ER/HIO iteration plays as the coarse feature reconstruction, whereas the KSVD-based inpainting technique deals with the fine feature set accordingly. …”
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A Novel Approach to Optimize Key Limitations of Azure Kinect DK for Efficient and Precise Leaf Area Measurement by Ziang Niu, Ting Huang, Chengjia Xu, Xinyue Sun, Mohamed Farag Taha, Yong He, Zhengjun Qiu

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Residual trio feature network for efficient super-resolution by Junfeng Chen, Mao Mao, Azhu Guan, Altangerel Ayush

Subjects: “…Image inpainting…”
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Photorealistic Texture Contextual Fill-In by Radek Richtr

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