Video completion in the presence of moving subjects based on segmentation using Neutrosophic sets by Amanna Ghanbari Talouki, Abbas Koochari, Seyyed Ahmad Edalatpanah

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ArtDiff: Integrating IoT and AI to enhance precision in ancient mural restoration by Yuhan Yan, Bowen Chai, Jiapeng Li

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SF-SAM-Adapter: SAM-based segmentation model integrates prior knowledge for gaze image reflection noise removal by Ting Lei, Jing Chen, Jixiang Chen

“…We achieved segmentation metrics of IoU (Intersection over Union) = 0.749 and Dice = 0.853 at a memory size of 13.9 MB, outperforming recent techniques, including UNet, UNet++, BATFormer, FANet, MSA, and SAM2-Adapter. In inpainting, we employ the advanced inpainting algorithm LAMA (Large Mask inpainting), resulting in significant improvements in gaze tracking accuracy by 0.502°, 0.182°, and 0.319° across three algorithms. …”
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TEC Map Completion Through a Deep Learning Model: SNP‐GAN by Yang Pan, Mingwu Jin, Shunrong Zhang, Yue Deng

“…Compared to the conventional image inpainting methods, the deep learning methods using generative adversarial networks (GANs) offer an effective image inpainting tool. …”
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TEC Map Completion Using DCGAN and Poisson Blending by Yang Pan, Mingwu Jin, Shunrong Zhang, Yue Deng

“…The advance of deep learning offers powerful tools to perform certain tasks in data science, such as image completion (or inpainting). Among them, deep convolutional generative adversarial network (DCGAN) is capable of learning the properties of the objects and recovering missing data effectively. …”
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Dealing with data gaps for TianQin with massive black hole binary signal by Lu Wang, Hong-Yu Chen, Xiangyu Lyu, En-Kun Li, Yi-Ming Hu

“…The easy-to-implement window function method can already perform well, except that it will sacrifice some SNR due to the adoption of the window. The inpainting method is slower, but it can minimize the impact of the data gap.…”
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Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review by Paul Rodríguez

“…Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with Gaussian noise into a more general technique for inverse problems such as deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. …”
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RGBD Scene Flow Estimation with Global Nonrigid and Local Rigid Assumption by Xiuxiu Li, Yanjuan Liu, Haiyan Jin, Lei Cai, Jiangbin Zheng

“…Firstly, the preprocessing is implemented, which includes the colour-depth registration and depth image inpainting, to processing holes and noises in the depth image; secondly, the depth image is segmented to obtain different motion regions with different depth values; thirdly, scene flow is estimated based on the global nonrigid and local rigid assumption and spatial-temporal correlation of RGBD information. …”
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Generating synthetic images for construction machinery data augmentation utilizing context-aware object placement by Yujie Lu, Bo Liu, Wei Wei, Bo Xiao, Zhangding Liu, Wensheng Li

“…To efficiently generate high-quality data, a synthesis method of construction data was proposed utilizing Unreal Engine (UE) and PlaceNet. First, the inpainting algorithm was applied to generate pure backgrounds, followed by multi-angle foreground capture within the UE. …”
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Global TEC Map Fusion Through a Hybrid Deep Learning Model: RFGAN by Zhou Chen, Kecheng Zhou, Haimeng Li, Jing‐song Wang, Zhihai Ouyang, Xiaohua Deng

“…To a certain extent, we inpainted the ocean area of MIT‐TEC through RFGAN. …”
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Prior-FOVNet: A Multimodal Deep Learning Framework for Megavoltage Computed Tomography Truncation Artifact Correction and Field-of-View Extension by Long Tang, Mengxun Zheng, Peiwen Liang, Zifeng Li, Yongqi Zhu, Hua Zhang

“…The sMVCT images, along with pre-corrected MVCT images obtained via sinogram extrapolation, are then input into a Swin Transformer-based image inpainting network for artifact correction and FOV extension. …”
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