Robust Kronecker product video denoising based on fractional-order total variation model

Chen, Gao; Zhang, Jiashu; Li, Defang; Chen, Huaixin

doi:10.1016/j.sigpro.2015.06.027

Cited by 19 publications

(9 citation statements)

References 31 publications

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“…For simplicity each

Z_{1}, Z_{2}, Z_{3}

, T and B in (12) are vectorised to column vectors. Alternating direction method of multipliers [32, 33] is adopted to solve the optimisation problem. In each iteration, we solve the optimisation problem by minimising each

Z_{1}, Z_{2}, Z_{3}

, T and B variables at a time and keeping the other variables fixed.…”

Section: Methodsmentioning

confidence: 99%

Reweighted infrared patch image model for small target detection based on non‐convex ℒp‐ norm minimisation and TV regularisation

Rawat

Verma

Kumar

2020

IET Image Processing

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“…For simplicity each

Z_{1}, Z_{2}, Z_{3}

Z_{1}, Z_{2}, Z_{3}

, T and B variables at a time and keeping the other variables fixed.…”

Section: Methodsmentioning

confidence: 99%

Reweighted infrared patch image model for small target detection based on non‐convex ℒp‐ norm minimisation and TV regularisation

Rawat

Verma

Kumar

2020

IET Image Processing

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“…It can be seen from Eq. (3) that the weight given to pixel y k (j) goes down as y k (i) − y k (j) 2 goes up. The weight also goes down with the spatial distance between pixel i and j.…”

Section: Single-frame Non-local Means Filtermentioning

confidence: 99%

“…In the case of some medical imagery, like x-ray images and video, short integration times are essential to limit the x-ray dose to the patient. While digital video may suffer from lower SNR, it also provides 3D data that often has significant temporal redundancy [2]. Video denoising algorithms seek to reduce noise by exploiting the both spatial and temporal correlation in the signal [1].…”

Section: Introductionmentioning

confidence: 99%

Recursive non-local means filter for video denoising

Ali

Hardie

2017

J Image Video Proc.

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In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window. The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video processing, can be computationally demanding. Note that the size of a 3D search window is the size of the 2D search window multiplied by the number of frames being used to form the output. Exploiting a large number of frames in this manner can be prohibitive for real-time video processing. Here, we propose a novel recursive NLM (RNLM) algorithm for video processing. Our RNLM method takes advantage of recursion for computational savings, compared with the direct 3D NLM. However, like the 3D NLM, our method is still able to exploit both spatial and temporal redundancy for improved performance, compared with 2D NLM. In our approach, the first frame is processed with single-frame NLM. Subsequent frames are estimated using a weighted sum of pixels from the current frame and a pixel from the previous frame estimate. Only the single best matching patch from the previous estimate is incorporated into the current estimate. Several experimental results are presented here to demonstrate the efficacy of our proposed method in terms of quantitative and subjective image quality.

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“…Application of PCA guarantees the exact preservation of fine texture and minute details. Chen et al [3] presented a Robust Kronecker Product Video Denoising (RKPVD) method using fractional-order total variation. The video model is projected to remove classical Gaussian-impulse noises from input video matrix data.…”

Section: IImentioning

confidence: 99%

Digital video denoising using transform domain techniques

2017

IJRTER

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Abstract-Utmost prevailing practical digital video denoising techniques depend upon a traditional statistical model of image noise, such as Independent and identically distributed random variablesGaussian noise, which is often violated in real life scenarios. For example, following foremost sources of video noise with dissimilar statistical distributions have been identified: photon shot noise, fixed pattern noise, amplifier noise, dark current noise and quantization noise. Performance of prevailing video denoising algorithms will severely degrade when the algorithm is applied on those images with noises evident from multiple sources. In this paper, a dictionary learning based scheme is proposed which computes the basis function adaptively from the first input image frames per fifty frames. Unlike other classical approaches like wavelet or contourlet transforms where the mother wavelet/basis functions are constant. If the mother wavelet/basis function is constant it is more likely that it will fail to capture the minuscule noise details from real life images. Therefore, the basis function is learnt from first frame itself. The dictionary learning method provides sparse representation of the image. Here, hard thresholding algorithm is applied to compute the denoised frame.

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Robust Kronecker product video denoising based on fractional-order total variation model

Cited by 19 publications

References 31 publications

Reweighted infrared patch image model for small target detection based on non‐convex ℒp‐ norm minimisation and TV regularisation

Reweighted infrared patch image model for small target detection based on non‐convex ℒp‐ norm minimisation and TV regularisation

Recursive non-local means filter for video denoising

Digital video denoising using transform domain techniques

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