Robust generalized low rank approximations of matrices for video denoising

Xian, Zhao; An, Gaoyun; Cen, Yigang; Wang, Hengyou; Zhao, Ruizhen

doi:10.1109/icsp.2016.7877944

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…The mux block in Fig. 4 selects the appropriate path according to the interval defined in (5). The symmetry ofr(l) was exploited in such a way that the slope coefficients in the first three intervals were sufficient for the computingr(l) for all possible values of l.…”

Section: Fig 2 Architecture For the Acf Estimatormentioning

confidence: 99%

“…Mux Fig. 4 Architecture for the implementation ofr( l), where the constants in (5) where approximated by the ones in Table 2 The designs were implemented using signed 10-bit words for representing the output estimates according to the ACF, Kedem, and the proposed estimator. Table 3 summarises the resource utilisation in terms of look-up table (LUT), flip-flops (FFs), and slices [17] and performance measurements expressed by maximum operating frequency, latency, and dynamic power.…”

Section: Fig 2 Architecture For the Acf Estimatormentioning

confidence: 99%

“…Due to the raising demand for digital signal processing (DSP) systems capable of operating at low power and low complexity, approximate methods have been considered for image processing [1, 2]. In particular, several approximate discrete transforms have been recently proposed [3–5] for image compression, where pixel data often stems from natural images and are modelled according to the first‐order autoregressive (

normalAR (1)

) process [6]. The

AR false(1 false)

model depends only on a single parameter, the correlation coefficient

ρ

, whose identification determines the suitable DSP tools for data analysis [7, 8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low‐complexity architecture for AR(1) inference

et al. 2020

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In this Letter, we propose a low-complexity estimator for the correlation coefficient based on the signed AR(1) process. The introduced approximation is suitable for implementation in low-power hardware architectures. Monte Carlo simulations reveal that the proposed estimator performs comparably to the competing methods in literature with maximum error in order of 10 −2. However, the hardware implementation of the introduced method presents considerable advantages in several relevant metrics, offering more than 95% reduction in dynamic power and doubling the maximum operating frequency when compared to the reference method.

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Section: Fig 2 Architecture For the Acf Estimatormentioning

confidence: 99%

Section: Fig 2 Architecture For the Acf Estimatormentioning

confidence: 99%

normalAR (1)

) process [6]. The

AR false(1 false)

model depends only on a single parameter, the correlation coefficient

ρ

, whose identification determines the suitable DSP tools for data analysis [7, 8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low‐complexity architecture for AR(1) inference

et al. 2020

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“…This application is the most investigated one. Indeed, numerous authors used RPCA problem formulations in applications such as background/foreground separation [4], [208], [223], background initialization [255], [258], moving target detection [241], motion saliency detection [47], [300], [332], motion estimation [238], visual object tracking [168] [276], action recognition [126], key frame extraction [60], video object segmentation [130], [153], [197], [317], [319], video coding [45], [46], [110], [331], video restoration and denoising [142], [334], [109], [318], [176], video inpainting [142], hyperspectral video processing [96], [42], and video stabilization [68].…”

Section: B Video Processingmentioning

confidence: 99%

“…This method can effectively remove the noise, but must transform two-dimensional samples to one-dimensional vectors and the input matrix should be approximatly low rank matrix. To remedy this limitation, Zhao et al [334] used an extended RPCA algorithm called Low Rank Approximations of Matrices (GLRAM) to obtain better performance than RPCA. As Ji et al [142], Guo and Vaswani [109] also considered that many noisy or corrupted videos can be split into three parts but they used the notion of layers instead of patches.…”

Section: J Video Restoration and Denoisingmentioning

confidence: 99%

On the Applications of Robust PCA in Image and Video Processing

et al. 2018

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Robust PCA (RPCA) via decomposition into lowrank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing and 3D computer vision. Indeed, most of the time these applications require to detect sparse outliers from the observed imagery data that can be approximated by a lowrank matrix. Moreover, most of the time experiments show that RPCA with additional spatial and/or temporal constraints often outperforms the state-of-the-art algorithms in these applications. Thus, the aim of this paper is to survey the applications of RPCA in computer vision. In the first part of this paper, we review representative image processing applications as follows: (1) lowlevel imaging such as image recovery and denoising, image composition, image colorization, image alignment and rectification, multi-focus image and face recognition, (2) medical imaging like dynamic Magnetic Resonance Imaging (MRI) for acceleration of data acquisition, background suppression and learning of inter-frame motion fields, and (3) imaging for 3D computer vision with additional depth information like in Structure from Motion (SfM) and 3D motion recovery. In the second part, we present the applications of RPCA in video processing which utilize additional spatial and temporal information compared to image processing. Specifically, we investigate video denoising and restoration, hyperspectral video and background/foreground separation. Finally, we provide perspectives on possible future research directions and algorithmic frameworks that are suitable for these applications.

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Online burr video denoising by learning sparsifying transform

Han

Liu

et al. 2019

IET Image Processing

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The burrs on high-voltage copper contact leads to point discharge and device damage. Since the high-voltage copper contact has different machining batch and the contour various which the machine tool remove is not economic and robot usually is used to remove the burrs of high-voltage copper contact. The first step for robot deburring is to identify burrs. In order to improve the performance of copper contact burr video denoising, this article presents online burr video denoising sparsifying transforms algorithm, which defined two alternative values between the optimal sparse signal and transform learning dictionary, simultaneously, calculated the mean of peak signal-to-noise ratio, the mean of execution time, the STD (STandard Deviation), and the VAR (VARiance), accordingly presented an burr video denoising algorithm and compared with state-of-the-art video denoising algorithms. The experiment results show that compared with traditional methods, the burr video denoising algorithm has higher denoising precision, faster denoising speed, and stronger high-noise-level processing capacity, and so on. The numeric experiments show that the proposed approach has higher peak signal-to-noise ratio and less computation complexity than the existing video denoising methods.

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Robust generalized low rank approximations of matrices for video denoising

Cited by 5 publications

References 11 publications

Low‐complexity architecture for AR(1) inference

Low‐complexity architecture for AR(1) inference

On the Applications of Robust PCA in Image and Video Processing

Online burr video denoising by learning sparsifying transform

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