Efficiency analysis of color image filtering

Fevralev, Dmitriy V.; Ponomarenko, Nikolay N.; Lukin, Vladimir; Abramov, Sergey; Егиазарян, Карен; Astola, Jaakko

doi:10.1186/1687-6180-2011-41

Cited by 40 publications

(49 citation statements)

References 49 publications

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“…This allows to improve a denoising performance and to diminish blocking artifacts with respect to the case of filtering performed in non-overlapping blocks. Note also, that in the case of fully overlapping blocks, DCT-based filtering efficiency is close to that of the state-of-the-art filters [23] such as, e.g., BM3D [37]. This is one more reason why the DCT-based filtering was selected in our analysis.…”

Section: Denoising Methods and Quantitative Criteriamentioning

confidence: 92%

“…In the next sections of the paper, we give quantitative definition of what is essential. We characterize image filtering efficiency not only by the conventional criteria, such as peak signal-to-noise ratio (PSNR) (or mean square error (MSE)), but also using metrics that describe image visual quality [23]. This is important since for many considered applications, visual quality of filtered images has a great importance (e.g., digital photography, medical imaging).…”

Section: Introductionmentioning

confidence: 99%

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On required accuracy of mixed noise parameter estimation for image enhancement via denoising

et al. 2014

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Characteristics of noise (type, statistics, spatial correlation) are nowadays exploited in many image denoising and enhancement methods. However, these characteristics are often unknown, and they have to be extracted from an image at hand. There are many powerful and accurate blind methods for noise variance estimation for the cases of additive and multiplicative noise models. However, more complicated noise models containing a mixture of signal-independent (SI) and signal-dependent (SD) components are often more adequate in practice. Parameters of both components have to be automatically estimated to be used in image enhancement. This paper addresses a question of required accuracy of such estimation. Analysis is carried out for color images processed by a filter based on discrete cosine transform. The influence of errors in mixed noise parameters estimation is studied in terms of filtering efficiency. This efficiency is characterized by the conventional criterion peak signal-to-noise ratio (PSNR) and two visual quality metrics, PSNR human visual system masking (PSNR-HVS-M) and multi-scale structural similarity (MSSIM). If a reduction of filtering efficiency exceeds 0.5 dB (in terms of PSNR and PSNR-HVS-M) or 0.005 (in terms of MSSIM), mixed noise parameters estimation is assumed to be unacceptable. As the result, it is shown that SI and SD noise parameters have to be estimated with a relative error not exceeding 20%…30%.

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Section: Denoising Methods and Quantitative Criteriamentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

On required accuracy of mixed noise parameter estimation for image enhancement via denoising

et al. 2014

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“…This equation (1)(2)(3)(4)(5)(6)(7)(8)(9) leads to the motivation for defining the PSD in (1-8). The PSD can also be defined as:…”

Section: Power Spectral Densitymentioning

confidence: 99%

“…These advantages have led to increasing interest in data-adaptive approaches towards the problem of spectral estimation. The proposed study thereby formulated a novel framework to access the performance efficiency of the conventional APES technique and determine the quality signal concerning PSD and computational complexity perspectives [4]. The study also gives insight into the in-depth performance analysis of conventional PSC, ASC and Capon estimation methods while improving the SNR as well as reducing the MSE of a nonstationary process.…”

Section: Introductionmentioning

confidence: 99%

Integrated Framework to Study Efficient Spectral Estimation Techniques for Assessing Spectral Efficiency Analysis

Prasad¹,

Suresh²

2017

ijacsa

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Abstract-The advanced network applications enable software driven spectral analysis of non-stationary signal or processes which precisely involves domain analysis with the purpose of decomposing a complex signal coefficients into simpler forms. However, the proper estimation of power coefficients over frequency components of a random signal leads to provide very useful information required in various fields of study. The complex design constraints associated with conventional parametric models such as Dynamic Average Model, Autoregressive MA, etc. for multidimensional spectral estimation using adaptive filters leads to a situation where higher computational complexities generate significant overhead on the systems. Therefore, the proposed study aims to formulate an efficient framework intended to derive a fast algorithm for processing Adaptive Capon and Phase Estimator (APES). The proposed method is applied to a non-stationary signal which is random. Further, the adaptive estimation of power spectra along with more accurate spectral efficiency has been identified in case of APES. An extensive performance evaluation followed by a comparative analysis has been performed by obtaining the values from different spectral estimation techniques, such as APES, PSC, ASC, and CAPON. Moreover, the framework ensures that unlike others, APES is subjected to attain superior signal quality regarding Power Spectral Density (PSD) and Signal to Noise Ratio (SNR) while achieving very less amount of Mean Square Error (MSE). It also exhibits comparatively low convergence speed and computational complexity as compared to its legacy versions.

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“…Therefore, noise reduction is regarded as one of the most frequently performed operations [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive rank weighted switching filter for impulsive noise removal in color images

Smołka

Malik

2012

J Real-Time Image Proc

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In this paper, a novel approach to the problem of impulsive noise removal in color digital images is presented. The described switching filter is based on the rank weighted, cumulated pixel dissimilarity measures, which are used for the detection of image samples contaminated by impulsive noise process. The introduced adaptive design enables the filter to tune its parameters to the amount of impulsive noise corrupting the image. The comparison with existing denoising schemes shows that the new technique more efficiently removes the impulses introduced by the noise process, while better preserving image details. An important feature of the new filter is its low computational complexity, which allows for its application in real-time applications.

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Efficiency analysis of color image filtering

Cited by 40 publications

References 49 publications

On required accuracy of mixed noise parameter estimation for image enhancement via denoising

On required accuracy of mixed noise parameter estimation for image enhancement via denoising

Integrated Framework to Study Efficient Spectral Estimation Techniques for Assessing Spectral Efficiency Analysis

Adaptive rank weighted switching filter for impulsive noise removal in color images

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