Dr. Nikolay N. Ponomarenko Profile

Dr. Nikolay N. Ponomarenko

at National Aerospace Univ

SPIE Involvement:

Author

Publications (33)

Proceedings Article | 17 March 2015 Paper

Combining full-reference image visual quality metrics by neural network

Vladimir Lukin, Nikolay Ponomarenko, Oleg Ieremeiev, Karen Egiazarian, Jaakko Astola

Proceedings Volume 9394, 93940K (2015) https://doi.org/10.1117/12.2085465

KEYWORDS: Image quality, Molybdenum, Visualization, Neural networks, Databases, Image visualization, Neurons, Image denoising, Statistical analysis, Fabry–Perot interferometers

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SPIE Journal Paper | 11 August 2014 Open Access

Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Alexander Zemliachenko, Ruslan Kozhemiakin, Mikhail Uss, Sergey Abramov, Nikolay Ponomarenko, Vladimir Lukin, Benoît Vozel, Kacem Chehdi

JARS, Vol. 8, Issue 01, 083571, (August 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083571

KEYWORDS: Image compression, Chromium, Hyperspectral imaging, 3D image processing, Data compression, Sensors, Signal to noise ratio, Interference (communication), Image processing, Data acquisition

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A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and “difference” images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15–20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.

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Proceedings Article | 19 February 2013 Paper

Visual quality analysis for images degraded by different types of noise

Nikolay Ponomarenko, Vladimir Lukin, Oleg Ieremeyev, Karen Egiazarian, Jaakko Astola

Proceedings Volume 8655, 86550E (2013) https://doi.org/10.1117/12.2000062

KEYWORDS: Visualization, Image quality, Databases, Interference (communication), Molybdenum, Visual analytics, Image analysis, Image visualization, Image processing, Image compression

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Proceedings Article | 2 February 2012 Paper

Performance evaluation for 2D and 3D filtering methods of noise removal in color images

Nikolay Ponomarenko, Vladimir Lukin, Alexander Zelensky, Karen Egiazarian, Jaakko Astola

Proceedings Volume 8295, 829506 (2012) https://doi.org/10.1117/12.906379

KEYWORDS: Image filtering, Optical filters, 3D image processing, Visualization, Image quality, Image processing, Digital filtering, Nonlinear filtering, Denoising, Databases

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Proceedings Article | 2 February 2012 Paper

Sharpness metric for no-reference image visual quality assessment

Nikolay Ponomarenko, Vladimir Lukin, Oleg Eremeev, Karen Egiazarian, Jaakko Astola

Proceedings Volume 8295, 829519 (2012) https://doi.org/10.1117/12.906393

KEYWORDS: Image quality, Visualization, Image visualization, Image compression, Molybdenum, Databases, Discrete wavelet transforms, Image analysis, Photography, Image processing

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Proceedings Article | 27 October 2011 Paper

Performance evaluation for blind methods of noise characteristic estimation for TerraSAR-X images

Vladimir Lukin, Sergey Abramov, Dmitriy Fevralev, Nikolay Ponomarenko, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi

Proceedings Volume 8180, 81800X (2011) https://doi.org/10.1117/12.897730

KEYWORDS: Speckle, Synthetic aperture radar, Image analysis, Error analysis, Statistical analysis, Image filtering, Image processing, Image segmentation, Edge detection, Image compression

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Proceedings Article | 4 February 2011 Paper

Efficiency analysis of DCT-based filters for color image database

Dmitriy Fevralev, Nikolay Ponomarenko, Vladimir Lukin, Sergey Abramov, Karen Egiazarian, Jaakko Astola

Proceedings Volume 7870, 78700R (2011) https://doi.org/10.1117/12.871944

KEYWORDS: Image filtering, Optical filters, Visualization, Databases, Image quality, Image processing, Denoising, Image visualization, Analytical research, Color image processing

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Images formed by different systems are often noisy which makes filtering a typical operation of image pre-processing. In many research papers, filter performance is analyzed for a limited number of standard test images and noise variances. Here we use a recently created color image database TID2008 that allows assessing filter efficiency for 25 color images corrupted by noise with different values of variance, both i.i.d. and spatially correlated. Besides, this image database serves the purpose of evaluating different quality metrics including those able to characterize visual quality of original and processed images considerably better than conventional MSE and PSNR. The study is carried out for filters based on discrete cosine transform (DCT) able to suppress both i.i.d. and spatially correlated noise depending upon a way of threshold setting. It is shown that improvement of PSNR (IPSNR) due to filtering is very close for R, G, and B components of color images and this improvement depends on image content. IPSNR reaches 9 dB for quite simple images and it is only about 1 dB for highly textural images if initial PSNR=30 dB. Note that IPSNR is larger if the original PSNR is smaller. The visual quality metric PSNR-HVS-M is studied as well. The metric PSNR-HVS-M becomes larger due to filtering but in smaller degree than PSNR does. We demonstrate that it is possible to forecast whether or not visual quality can be improved due to filtering or to detect in advance highly textural images for which filtering can be not efficient enough. The provided output MSEs are also compared to potential limits calculated according to the recently proposed methodology. It is demonstrated that for highly textural images the DCT filtering with 8x8 full overlapping blocks and hard thresholding provides output MSE close to potential limits. The provided and limit MSEs differ from each other by about 10%. For simpler images, the provided and limit MSEs can differ by 1.5...2.5 times. Analysis is also carried out for spatially correlated noise. It is shown that efficiency of filtering in this case is lower.

Proceedings Article | 4 February 2011 Paper

Color image lossy compression based on blind evaluation and prediction of noise characteristics

Nikolay Ponomarenko, Vladimir Lukin, Karen Egiazarian, Leena Lepisto

Proceedings Volume 7870, 78700S (2011) https://doi.org/10.1117/12.872009

KEYWORDS: Image compression, Image quality, Visualization, Image analysis, Chromium, Digital cameras, Quantization, Visual compression, Image processing, Molybdenum

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SPIE Journal Paper | 1 January 2011

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

Vladimir Lukin, Sergey Abramov, Nikolay Ponomarenko, Mikhail Uss, Mikhail Zriakhov, Benoit Vozel, Kacem Chehdi, Jaakko Astola

JARS, Vol. 5, Issue 1, 053502, (January 2011) https://doi.org/10.1117/12.10.1117/1.3539768

KEYWORDS: Image processing, Image compression, Image filtering, Interference (communication), Sensors, Lab on a chip, Edge detection, Chromium, Digital filtering, Remote sensing

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Proceedings Article | 23 October 2010 Paper

Classification of filtered multichannel images

Dmitriy Fevralev, Vladimir Lukin, Nikolay Ponomarenko, Benoit Vozel, Kacem Chehdi, Andriy Kurekin, Lik-Kwan Shark

Proceedings Volume 7830, 78300M (2010) https://doi.org/10.1117/12.864215

KEYWORDS: Image filtering, Image classification, Remote sensing, 3D image processing, Linear filtering, Visualization, Filtering (signal processing), Denoising, Landsat, Earth observing sensors

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SPIE Journal Paper | 1 April 2010 Open Access

Discrete cosine transform−based local adaptive filtering of images corrupted by nonstationary noise

Vladimir Lukin, Dmitriy Fevralev, Nikolay Ponomarenko, Sergey Abramov, Oleksiy Pogrebnyak, Karen Egiazarian, Jaakko Astola

JEI, Vol. 19, Issue 02, 023007, (April 2010) https://doi.org/10.1117/12.10.1117/1.3421973

KEYWORDS: Image filtering, Digital filtering, Nonlinear filtering, Image processing, Interference (communication), Radar, Electronic filtering, Visualization, Statistical analysis, Denoising

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Proceedings Article | 8 February 2010 Paper

A method for blind estimation of spatially correlated noise characteristics

Nikolay Ponomarenko, Vladimir Lukin, Karen Egiazarian, Jaakko Astola

Proceedings Volume 7532, 753208 (2010) https://doi.org/10.1117/12.847986

KEYWORDS: Image filtering, Image quality, Image processing, Image analysis, Visualization, Remote sensing, Denoising, Gaussian filters, Image sensors, Imaging systems

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Proceedings Article | 28 September 2009 Paper

Processing of images based on blind evaluation of noise type and characteristics

Vladimir Lukin, Sergey Abramov, Nikolay Ponomarenko, Mikhail Uss, Benoit Vozel, Kacem Chehdi, Jaakko Astola

Proceedings Volume 7477, 74770B (2009) https://doi.org/10.1117/12.830058

KEYWORDS: Image compression, Image filtering, Image processing, Sensors, Edge detection, Statistical analysis, Lab on a chip, Error analysis, Interference (communication), Digital filtering

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Proceedings Article | 11 February 2009 Paper

Local adaptive filtering of images corrupted by nonstationary noise

Vladimir Lukin, Dmitriy Fevralev, Nikolay Ponomarenko, Oleksiy Pogrebnyak, Karen Egiazarian, Jaakko Astola

Proceedings Volume 7245, 724506 (2009) https://doi.org/10.1117/12.805298

KEYWORDS: Image filtering, Digital filtering, Image processing, Radar, Filtering (signal processing), Nonlinear filtering, Visualization, Hyperspectral imaging, Imaging systems, Remote sensing

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Proceedings Article | 10 October 2008 Paper

Filtering of radar images based on blind evaluation of noise characteristics

Vladimir Lukin, Nikolay Ponomarenko, Sergey Abramov, Benoit Vozel, Kacem Chehdi, Jaakko Astola

Proceedings Volume 7109, 71090R (2008) https://doi.org/10.1117/12.799396

KEYWORDS: Image filtering, Radar, Image segmentation, Filtering (signal processing), Statistical analysis, Image processing, Error analysis, Lab on a chip, X band, Electronic filtering

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Proceedings Article | 10 October 2008 Paper

Compression and classification of noisy multichannel remote sensing images

Vladimir Lukin, Nikolay Ponomarenko, Alexander Zelensky, Andriy Kurekin, Ken Lever

Proceedings Volume 7109, 71090W (2008) https://doi.org/10.1117/12.799497

KEYWORDS: Image compression, Remote sensing, Image classification, Chromium, Picosecond phenomena, Landsat, Data compression, JPEG2000, Control systems, Earth observing sensors

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Proceedings Article | 3 March 2008 Paper

Adaptive DCT-based filtering of images corrupted by spatially correlated noise

Nikolay Ponomarenko, Vladimir Lukin, Aleksandr Zelensky, Jaakko Astola, Karen Egiazarian

Proceedings Volume 6812, 68120W (2008) https://doi.org/10.1117/12.764893

KEYWORDS: Image filtering, Digital filtering, Image processing, Denoising, Imaging systems, Optical filters, Nonlinear filtering, Visualization, Image denoising, Gaussian filters

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Proceedings Article | 26 February 2008 Paper

Image filter effectiveness characterization based on HVS

Vladimir Lukin, Nikolay Ponomarenko, Sergey Krivenko, Karen Egiazarian, Jaakko Astola

Proceedings Volume 6814, 68140Z (2008) https://doi.org/10.1117/12.774716

KEYWORDS: Image filtering, Image quality, Image processing, Digital filtering, Visualization, Image visualization, Nonlinear filtering, Optical filters, Image analysis, Image quality standards

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Proceedings Article | 28 February 2007 Paper

Compression of noisy Bayer pattern color filter array images

Vladimir Lukin, Nikolay Ponomarenko, Andriy Bazhyna, Karen Egiazarian

Proceedings Volume 6498, 64980K (2007) https://doi.org/10.1117/12.715588

KEYWORDS: Image compression, Denoising, Chromium, Image processing, Quantization, Image quality, Digital cameras, Cameras, Optical filters, JPEG2000

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Proceedings Article | 27 February 2007 Paper

Prediction of signs of DCT coefficients in block-based lossy image compression

Nikolay Ponomarenko, Andriy Bazhyna, Karen Egiazarian

Proceedings Volume 6497, 64970L (2007) https://doi.org/10.1117/12.713872

KEYWORDS: Image compression, Quantization, Analytical research, Phase modulation, Chromium, Wavelets, Video compression, JPEG2000, Image quality, Digital photography

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Proceedings Article | 27 February 2007 Paper

Object classification, segmentation, and parameter estimation in multichannel images by classifier learning with clustering of local parameters

Vladimir Lukin, Nikolay Ponomarenko, Alexander Zelensky, Karen Egiazarian, Jaakko Astola

Proceedings Volume 6497, 649712 (2007) https://doi.org/10.1117/12.705143

KEYWORDS: Image segmentation, Image classification, Image filtering, RGB color model, Optical filters, Image processing, Image analysis, Digital filtering, Nonlinear filtering, Quantitative analysis

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Proceedings Article | 29 September 2006 Paper

Quasi-optimal compression of noisy optical and radar images

Vladimir Lukin, Nikolay Ponomarenko, Mikhail Zriakhov, Alexander Zelensky, Karen Egiazarian, Jaakko Astola

Proceedings Volume 6365, 63650N (2006) https://doi.org/10.1117/12.689557

KEYWORDS: Image compression, Chromium, Remote sensing, Radar, JPEG2000, Lab on a chip, Image filtering, Data compression, Denoising, Data communications

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Proceedings Article | 1 March 2005 Paper

Locally adaptive image filtering based on learning with clustering

Nikolay Ponomarenko, Vladimir Lukin, Alexander Zelensky, Karen Egiazarian, Jaakko Astola

Proceedings Volume 5672, (2005) https://doi.org/10.1117/12.583222

KEYWORDS: Image filtering, Digital filtering, Image processing, Optical filters, Nonlinear filtering, Radar, Filtering (signal processing), Remote sensing, Image denoising, Numerical simulations

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Proceedings Article | 28 May 2004 Paper

Robust Lee local statistic filter for removal of mixed multiplicative and impulse noise

Nikolay Ponomarenko, Vladimir Lukin, Karen Egiazarian, Jaakko Astola

Proceedings Volume 5298, (2004) https://doi.org/10.1117/12.529123

KEYWORDS: Image filtering, Digital filtering, Optical filters, Image processing, Filtering (signal processing), Speckle, Nonlinear filtering, Finite impulse response filters, Numerical simulations, Imaging systems

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Proceedings Article | 5 February 2004 Paper

Image processing with texture feature preservation by three-state locally adaptive filter

Vladimir Lukin, Oleg Tsymbal, Nikolay Ponomarenko, Karen Egiazarian, Jaakko Astola

Proceedings Volume 5238, (2004) https://doi.org/10.1117/12.511518

KEYWORDS: Digital filtering, Image filtering, Image processing, Filtering (signal processing), Radar, Optical filters, Switching, Statistical analysis, Remote sensing, Compound parabolic concentrators

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Proceedings Article | 28 May 2003 Paper

Blind evaluation of additive noise variance in textured images by nonlinear processing of block DCT coefficients

Nikolay Ponomarenko, Vladimir Lukin, Sergey Abramov, Karen Egiazarian, Jaakko Astola

Proceedings Volume 5014, (2003) https://doi.org/10.1117/12.477717

KEYWORDS: Image processing, Statistical analysis, Lab on a chip, Image analysis, Niobium, Spatial frequencies, Analog electronics, Numerical simulations, Interference (communication), Signal processing

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Proceedings Article | 7 March 2003 Paper

Automated robust procedure for SAR image filtering and preliminary analysis

Vladimir Lukin, Nikolay Ponomarenko, Sergey Abramov, Karen Egiazarian, Jaakko Astola

Proceedings Volume 4883, (2003) https://doi.org/10.1117/12.463075

KEYWORDS: Image filtering, Speckle, Image processing, Synthetic aperture radar, Digital filtering, Optical filters, Radar, Image analysis, Statistical analysis, Finite impulse response filters

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Proceedings Article | 22 May 2002 Paper

Digital elevation map reconstruction from isogram map using iterative DCT algorithm with nonlinear contraints

Nikolay Ponomarenko, Vladimir Lukin, Karen Egiazarian, Jaakko Astola

Proceedings Volume 4667, (2002) https://doi.org/10.1117/12.468002

KEYWORDS: Reconstruction algorithms, Image processing, Remote sensing, Digital filtering, Data communications, Linear filtering, Nonlinear filtering, Analytical research, Telecommunications, Signal attenuation

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Proceedings Article | 8 May 2001 Paper

Two-stage radar image despeckling based on local statistic Lee and sigma filtering

Vladimir Lukin, Nikolay Ponomarenko, Leonid Alekseyev, Vladimir Melnik, Jaakko Astola

Proceedings Volume 4304, (2001) https://doi.org/10.1117/12.424967

KEYWORDS: Image filtering, Digital filtering, Radar, Speckle, Optical filters, Image processing, Synthetic aperture radar, Statistical analysis, Numerical simulations, Gaussian filters

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Proceedings Article | 3 April 2000 Paper

Data fusion and processing for airborne multichannel system of radar remote sensing: methodology, stages, and algorithms

Vladimir Lukin, Jaakko Astola, Vladimir Melnik, Andrei Kurekin, Alexander Zelensky, Gennady Kulemin, Nikolay Ponomarenko, Alexander Dolia, Jussi Parkkinen

Proceedings Volume 4051, (2000) https://doi.org/10.1117/12.381634

KEYWORDS: Image filtering, Remote sensing, Digital filtering, Image processing, Radar, Data processing, Image classification, Optical filters, Ka band, Multichannel imaging systems

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Proceedings Article | 3 March 2000 Paper

Applications and properties of sigma and mean filters with adaptive window size

Nikolay Ponomarenko, Vladimir Lukin, Alexander Zelensky, Pertti Koivisto, Jaakko Astola

Proceedings Volume 3961, (2000) https://doi.org/10.1117/12.379402

KEYWORDS: Digital filtering, Image filtering, Optical filters, Medium wave, Radar, Image processing, Filtering (signal processing), Data processing, Linear filtering, Nonlinear filtering

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Proceedings Article | 6 April 1998 Paper

Nonlinear algorithms of isogram extraction from topographic maps

Vladimir Lukin, Nikolay Ponomarenko, Andrei Kurekin, Alexander Zelensky, Jaakko Astola, Pertti Koivisto, Kari Saarinen

Proceedings Volume 3304, (1998) https://doi.org/10.1117/12.304615

KEYWORDS: Digital filtering, Nonlinear filtering, Scanners, Optical filters, Vegetation, Feature extraction, Image processing, Binary data, Radar, RGB color model

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Proceedings Article | 25 March 1996 Paper

Algorithms of image nonlinear adaptive filtering using fragment recognition by expert system

Vladimir Lukin, Nikolay Ponomarenko, Jaakko Astola, Kari Saarinen

Proceedings Volume 2662, (1996) https://doi.org/10.1117/12.235830

KEYWORDS: Digital filtering, Optical filters, Image filtering, Linear filtering, Image processing, Detection and tracking algorithms, Nonlinear filtering, Finite impulse response filters, Complex systems, Statistical analysis

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Showing 5 of 33 publications

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