Video Inpainting Based on Re-weighted Tensor Decomposition

Ravindran, Anjali; Baburaj, M; George, Sudhish N.

doi:10.1007/978-981-10-7895-8_21

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…By representing visual as tensors, models addressing inverse problems exploit low-rank tensor decompositions (Section IV-B), or rely on robust variants (Sections IV-C IV-E) to estimate a low-rank tensor which forms a basis from which missing data can be imputed or denoised. Along this line of research, several methods for image and video inpainting and/or denoising have been developed by estimating the low-rank tensor using either nuclear norm-based regularizers, or by assuming an explicit low-rank tensor structure described by decompositions such CP, Tucker, Tensor Ring, or TT decomposition [218], [219], [220], [221], [92], [99], [100], [101], [98]. For the same task, methods based on tensor-structured separable dictionary learning (Section IV-E) have also been proposed [4], [115], [11].…”

Section: Tensor Methods In Inverse Problemsmentioning

confidence: 99%

Tensor Methods in Computer Vision and Deep Learning

et al. 2021

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Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic spaces and high-order interactions, tensors have a long history of applications in a wide span of computer vision problems. With the advent of the deep learning paradigm shift in computer vision, tensors have become even more fundamental. Indeed, essential ingredients in modern deep learning architectures, such as convolutions and attention mechanisms, can readily be considered as tensor mappings. In effect, tensor methods are increasingly finding significant applications in deep learning, including the design of memory and compute efficient network architectures, improving robustness to random noise and adversarial attacks, and aiding the theoretical understanding of deep networks.This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on visual data analysis and computer vision applications. Concretely, besides fundamental work in tensor-based visual data analysis methods, we focus on recent developments that have brought on a gradual increase of tensor methods, especially in deep learning architectures, and their implications in computer vision applications. To further enable the newcomer to grasp such concepts quickly, we provide companion Python notebooks, covering key aspects of the paper and implementing them, step-by-step with TensorLy.

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Section: Tensor Methods In Inverse Problemsmentioning

confidence: 99%

Tensor Methods in Computer Vision and Deep Learning

et al. 2021

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“…They have the characteristics of moving objects, which will seriously reduce the accuracy of moving object detection algorithms. In addition, due to the excellent performance of the tensor-based RPCA method, this method has also been successfully applied to the field of image and video denoising [56], [57]. Therefore, we use the tensor-based RPCA model to decompose the video and further restrict the snowflakes to achieve snow removal.…”

Section: A Problem Modelmentioning

confidence: 99%

Video Snow Removal Based on Self-Adaptation Snow Detection and Patch-Based Gaussian Mixture Model

et al. 2020

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Video desnowing has become a challenging research topic in computer vision in recent years. Existing methods cannot remove most of the snow in heavy snow scenes and will cause the deformation of moving objects when used for snowy videos that include moving objects. These methods have poor generalizability, exhibiting poor performance when removing snow from videos with different resolutions. In this paper, we propose a new video snow removal method based on self-adaptation snow detection and a patch-based Gaussian mixture model (VSRSG). First, an optical flow estimation method and a support vector machine (SVM) are used to detect snowflakes, and a self-adaptation threshold process is used to remove dense snowflakes in the snowflake detection map to obtain a sparse snowflake detection map. Then, a patch-based Gaussian mixture model (PBGMM), which can remove moving objects and both sparse and dense snowflakes from videos and restore a clear video background, is applied for background modeling. A Markov random field (MRF) and self-adaptation threshold processing are used to extract sparse snowflakes and moving objects and combine them with the background to form an input video without dense snowflakes. Finally, a similar block matching method is employed to fill in the detected snowflake pixels with the information from adjacent frames to remove the sparse snowflakes in the near range. This method can also remove snowflakes in front of moving objects. Experiments show that the proposed method can simultaneously remove sparse snowflakes, dense snowflakes and snowflakes in front of moving objects and outperforms other state-of-the-art methods.INDEX TERMS Video desnowing, self-adaptation snowflake detection, patch-based Gaussian mixture model, low-rank background modeling, moving foreground detection.

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Video Inpainting Based on Re-weighted Tensor Decomposition

Cited by 2 publications

References 14 publications

Tensor Methods in Computer Vision and Deep Learning

Tensor Methods in Computer Vision and Deep Learning

Video Snow Removal Based on Self-Adaptation Snow Detection and Patch-Based Gaussian Mixture Model

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