Methods and automatic procedures for processing images based on blind evaluation of noise type and characteristics

Abramov, Sergey; Ponomarenko, Nikolay N.; Uss, Mikhail; Zriakhov, Mikhail; Vozel, Benôıt; Chehdi, Kacem; Astola, Jaakko

doi:10.1117/1.3539768

Cited by 42 publications

(35 citation statements)

References 59 publications

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“…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]. Next, for many applications there is a need to carry out image processing in automatic (fully blind), robust, adaptive, intelligent way, better suited for solving any final task [22][23][24][25]. This is especially crucial when there is a need to process a large number of images, e.g., multichannel images and/or remote data on-board.…”

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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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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“…General requirements to the methods intended for blind (automatic) estimation of additive and multiplicative noise variance can be found in [8,17]. Clearly, it is desirable to provide almost unbiased estimates with estimation variance as small as possible.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, for the methods of pure additive or multiplicative noise variance estimation, it has been established that the estimation relative error in practice should not be larger than ±20% [8,18]. If an obtained variance estimate is outside this limit, under-or oversmoothing takes place in image denoising based on blind estimation result.…”

Section: Introductionmentioning

confidence: 99%

“…for further exploitation (e.g., object recognition, visual inspection, diagnostics, etc.). In order to enhance an image using, e.g., modern image denoising methods based on wavelets [5][6][7] or discrete cosine transform (DCT) [8,9] transforms, one has to know a noise type and its basic characteristics such as probability density function (PDF), variance, or two-dimensional (2D) spatial correlation function (if the observed noise is not independent and identically distributed (i.i.d.)). Proper thresholds in edge detection and image segmentation depend on noise statistics as well [1,10].…”

Section: Introductionmentioning

confidence: 99%

“…With a new generation of sensors, signal-dependent noise models have become more popular since they describe noise statistics better [8,9,[11][12][13]15,16,19,20]. There is an essential interest in design and testing the methods for blind estimation of mixed noise parameters [9,12,15,16,21,22].…”

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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