Prediction of filtering efficiency for DCT-based image denoising

Abramov, Sergey; Krivenko, Sergey; Roenko, Alexey; Lukin, Vladimir; Djurović, Igor; Чобану, М. К.

doi:10.1109/meco.2013.6601327

Cited by 31 publications

(45 citation statements)

References 15 publications

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“…Secondly, it has been already demonstrated that IPSNR for many¯lters can be predicted quite accurately for DCT-based¯lters 1,33,38,51 and some other denoising techniques. 34 Accuracy can be characterized di®erently where the root mean square error (RMSE) of estimates is one of the most adequate and widely used quantitative criteria.…”

Section: Filtering E±ciency Predictionmentioning

confidence: 96%

“…An idea that such indicators of denoising e±ciency can be predicted (estimated before image denoising is applied) has been put forward in papers. 1,8 The way proposed in Ref. 8 requires considerable computations and, thus, the time needed to derive a prediction indicator is comparable to a¯ltering procedure itself that restricts the practical application of this approach.…”

mentioning

confidence: 99%

“…1,35,36,34 showing that the accuracy depends upon many factors including what are the input and output parameters, how regression is calculated, what¯lter is analyzed, etc. Good prediction characteristics are already obtained for many modern¯lters if IPSNR is predicted.…”

mentioning

confidence: 99%

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Is Texture Denoising Efficiency Predictable?

Rubel

Abramov

Vozel

et al. 2017

Int. J. Patt. Recogn. Artif. Intell.

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Images of di®erent origin contain textures, and textural features in such regions are frequently employed in pattern recognition, image classi¯cation, information extraction, etc. Noise often present in analyzed images might prevent a proper solution of basic tasks in the aforementioned applications and is worth suppressing. This is not an easy task since even the most advanced denoising methods destroy texture in a more or less degree while removing noise. Thus, it is desirable to predict the¯ltering behavior before any denoising is applied. This paper studies the e±ciency of texture image denoising for di®erent noise intensities and several¯lter types under di®erent visual quality criteria (quality metrics). It is demonstrated that the most e±cient existing¯lters provide very similar results. From the obtained results, it is possible to generalize and employ the prediction strategy earlier proposed for denoising techniques based on the discrete cosine transform. Accuracy of such a prediction is studied and the ways to improve it

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Section: Filtering E±ciency Predictionmentioning

confidence: 96%

mentioning

confidence: 99%

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Is Texture Denoising Efficiency Predictable?

Rubel

Abramov

Vozel

et al. 2017

Int. J. Patt. Recogn. Artif. Intell.

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“…Suppose we have a method (procedure) that allows predicting filtering efficiency and deciding in advance (before starting filtering) whether it is worth applying filtering or not [14,15]. If such a method (procedure) is fast (performs considerably faster than filtering itself) and is accurate enough, it can be a useful tool in image processing (especially for hardware or software operating in automatic or semi-automatic mode) [16].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some steps towards designing prediction procedures have been done [15,[17][18][19][20][21]. In [17], it has been shown that for non-local denoising methods applied to remove additive white Gaussian noise (AWGN) it is possible to predict potential output mean square error (MSE) without having the corresponding reference image.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Despeckling Efficiency of DCT-Based Filters Applied to SAR Images

Rubel¹,

Medeiros

2015

2015 International Conference on Distributed Computing in Sensor Systems

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This paper deals with one way to predict efficiency of despeckling for images acquired by a single-or multi-look synthetic aperture radar (SAR). A particular filter based on discrete cosine transform (DCT) and adapted to speckle characteristics is considered. Two quantitative criteria characterizing despeckling efficiency are analyzed. It is shown that even one parameter that can be calculated for image blocks sequentially or in parallel allows carrying out a rather accurate prediction. Moreover, such a prediction is possible for both spatially uncorrelated and correlated speckle.

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Efficiency of DCT-Based Denoising Techniques Applied to Texture Images

Rubel¹,

Pogrebnyak

2014

Lecture Notes in Computer Science

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Textures or high-detailed structures contain information that can be exploited in pattern recognition and classification. If an acquired image is noisy, noise removal becomes an operation to improve image quality before further stages of processing. Among possible variants of denoising, we consider filters based on orthogonal transforms, in particular, on discrete cosine transform (DCT) known to be able to effectively remove additive white Gaussian noise (AWGN). Besides, we study a representative of nonlocal denoising techniques, namely, BM3D known as state-of-the-art technique based on DCT and similar patch search. We show that noise removal in texture images using the considered DCT-based techniques can distort fine texture details. To detect such situations and avoid texture degradation due to filtering, we propose to apply filtering efficiency prediction tests applicable to wide class of images. These tests are based on DCT coefficient statistic parameters and can be used for decision-making in relation to the use of the considered filters.

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Prediction of filtering efficiency for DCT-based image denoising

Cited by 31 publications

References 15 publications

Is Texture Denoising Efficiency Predictable?

Is Texture Denoising Efficiency Predictable?

Prediction of Despeckling Efficiency of DCT-Based Filters Applied to SAR Images

Efficiency of DCT-Based Denoising Techniques Applied to Texture Images

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