Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

Zhang, Yongxia; Liu, Yi; Li, Xuemei; Zhang, Caiming

doi:10.1007/s41095-015-0005-5

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…As the future work, we will extend our approach to the videos with notably dynamic scenes, i.e., dealing with motion blur caused by fast moving objects, possibly by integrating some visual enhancement techniques like [13], [43], [44]. Besides, our approach is suited for parallel processing due to the use of separable bundled kernels, so we plan to further improve its computation efficiency based on GPU programming, especially for the steps of kernels estimation and deconvolution.…”

Section: Discussionmentioning

confidence: 99%

Bundled Kernels for Nonuniform Blind Video Deblurring

Zhou

Huang

2017

IEEE Trans. Circuits Syst. Video Technol.

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We present a novel blind video deblurring approach by estimating a bundle of kernels and applying the residual deconvolution. Our approach adopts multiple kernels to represent spatially varying motion blur, thus can cope with non-uniform video deblurring. For each blurred frame, we build a warpingbased, space-variant motion blur model based on a bundle of homographies in between its adjacent frames. Then, the nearest sharp frame is employed to form a unblurred-blurred pair for solving the motion model, and obtain a bundle of kernels at the blurred frame. Finally, we apply the deconvolution on the residual between the warped unblurred frame and the blurred frame with the kernels. The blur kernel estimation and residual deconvolution are iteratively performed toward the deblurred frame, as well as significantly reducing artifacts like ringings. Experiments show that our approach can efficiently remove the non-uniform video blurring, and achieves better deblurring results than some state-of-the-art methods.

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Section: Discussionmentioning

confidence: 99%

Bundled Kernels for Nonuniform Blind Video Deblurring

Zhou

Huang

2017

IEEE Trans. Circuits Syst. Video Technol.

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“…The problem generally occurs due to transmission medium, temperature or any other corruption occurs due to unauthorized access. [8] Zhang et al 2015 highlight the algorithm based on low-rank matrix recovery to remove salt & pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information.…”

Section: Literature Surveymentioning

confidence: 99%

Fruit Disease Detection Using Convolution Neural Network Approach

Shivani¹,

Singh²

2018

AJCST

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Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.

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“…Recently, low rank matrix denoising algorithms have been widely studied [11][12][13][14][15]. The traditional, image denoising algorithms based on low rank matrix recovery only have the low rank constraint.…”

Section: Liangmentioning

confidence: 99%

Removing mixed noise in low rank textures by convex optimization

Liang

2016

Comp. Visual Media

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This paper introduces a new low rank texture image denoising algorithm, which can restore low rank texture contaminated by both Gaussian and salt-and-pepper noise. The algorithm formulates texture image denoising in terms of solving a low rank matrix optimization problem. Simply assuming low rank is insufficient to describe the properties of natural images, causing high noise amplitudes which lead to unsatisfactory denoising results or serious loss of image details. Thus, in addition to the low rank assumption, the continuity of natural images is also assumed by the algorithm, by adding a total variation regularizer to the optimization objective function. We further give an effective algorithm to solve this optimization problem. By combining the low rank and continuity assumptions, the proposed algorithm overcomes the deficiencies of using either the low rank assumption or total variation regularization alone. Experiments show that our algorithm can effectively remove mixed noise in low rank texture images, and is better than existing algorithms in both its subjective visual effects and in terms of quantitative objective measures.

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Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

Cited by 16 publications

References 14 publications

Bundled Kernels for Nonuniform Blind Video Deblurring

Bundled Kernels for Nonuniform Blind Video Deblurring

Fruit Disease Detection Using Convolution Neural Network Approach

Removing mixed noise in low rank textures by convex optimization

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