Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal

Öktem, R.; Егиазарян, Карен; Lukin, Vladimir; Ponomarenko, Nikolay N.; Tsymbal, Oleg V.

doi:10.1155/2007/42472

Cited by 54 publications

(55 citation statements)

References 32 publications

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“…25 Efficiency and usefulness of the local DCT commonly carried out in 8 × 8 pixel blocks has also been proven for image denoising applications in Refs. [26][27][28][29][30][31]. Thus, below we focus just on DCT as the considered basic orthogonal transform.…”

Section: Introductionmentioning

confidence: 99%

Wiener discrete cosine transform-based image filtering

Pogrebnyak¹

2012

J. Electron. Imaging

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Abstract. A classical problem of additive white (spatially uncorrelated) Gaussian noise suppression in grayscale images is considered. The main attention is paid to discrete cosine transform (DCT)-based denoising, in particular, to image processing in blocks of a limited size. The efficiency of DCT-based image filtering with hard thresholding is studied for different sizes of overlapped blocks. A multiscale approach that aggregates the outputs of DCT filters having different overlapped block sizes is proposed. Later, a two-stage denoising procedure that presumes the use of the multiscale DCT-based filtering with hard thresholding at the first stage and a multiscale Wiener DCT-based filtering at the second stage is proposed and tested. The efficiency of the proposed multiscale DCT-based filtering is compared to the state-of-the-art block-matching and three-dimensional filter. Next, the potentially reachable multiscale filtering efficiency in terms of output mean square error (MSE) is studied. The obtained results are of the same order as those obtained by Chatterjee's approach based on nonlocal patch processing. It is shown that the ideal Wiener DCTbased filter potential is usually higher when noise variance is high.

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Section: Introductionmentioning

confidence: 99%

Wiener discrete cosine transform-based image filtering

Pogrebnyak¹

2012

J. Electron. Imaging

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“…New mathematical fundamentals for filtering have appeared steadily during the last 40 years as robust estimation theory in 70 and 80th [10,13], wavelets, PCA and ICA in 90th of the previous century [11,14] have been developed. Also, many new methods of locally adaptive and non-local techniques of image filtering have been designed recently (see [8,[15][16][17] and references therein). The third reason is that more accurate and adequate models of noise have been designed and new practical situations for which the already designed filters perform poorly have been found [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…A lot of vector filters that allow exploiting inherent inter-channel correlation of color image components have been proposed since then [6,12]. In this article, we basically consider DCT-based filters [15,33,35,47] since they have shown themselves to be quite simple, efficient, and easily adaptable to processing grayscale and color images corrupted by i.i.d. and spatially correlated noise.…”

Section: Introductionmentioning

confidence: 99%

Efficiency analysis of color image filtering

Fevralev

Ponomarenko

Lukin

et al. 2011

EURASIP J. Adv. Signal Process.

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This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.

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“…As it is according to the metric PSNR, PSNR-HVS-M increases for the recommended setting of the DCT filter parameter β (compare PSNR-HVS-M (δ V = 0, δ k = 0) to PSNR-HVS-M(δ V = −1, δ k = −1)). Visual quality improvement due to filtering is essential -from Table 3 Simulation data for the test image #13, noise model (1), PSNR metric [35].…”

Section: Analysis Of Resultsmentioning

confidence: 99%

“…Since the DCT-based filtering allows to do this easily [34,35], we concentrate below on this method of denoising. The DCT-based filtering [34,35] is carried out in blocks of a limited support, usually 8 × 8 pixels. This feature of the DCT-based filtering allows easy adaptation to non-stationary (signal-dependent) properties of the noise.…”

Section: Denoising Methods and Quantitative Criteriamentioning

confidence: 99%

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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Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal

Cited by 54 publications

References 32 publications

Wiener discrete cosine transform-based image filtering

Wiener discrete cosine transform-based image filtering

Efficiency analysis of color image filtering

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

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