Dr. Nikolay N. Ponomarenko Profile

Dr. Nikolay N. Ponomarenko

at National Aerospace Univ

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Author

Publications (33)

PROCEEDINGS ARTICLE | March 17, 2015

Combining full-reference image visual quality metrics by neural network

Proc. SPIE. 9394, Human Vision and Electronic Imaging XX

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

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SPIE Journal Paper | August 11, 2014

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

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

JRS Vol. 8 Issue 01

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 | February 19, 2013

Visual quality analysis for images degraded by different types of noise

Proc. SPIE. 8655, Image Processing: Algorithms and Systems XI

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

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PROCEEDINGS ARTICLE | February 2, 2012

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

Proc. SPIE. 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II

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

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PROCEEDINGS ARTICLE | February 2, 2012

Sharpness metric for no-reference image visual quality assessment

Proc. SPIE. 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II

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

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PROCEEDINGS ARTICLE | October 27, 2011

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

Proc. SPIE. 8180, Image and Signal Processing for Remote Sensing XVII

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

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PROCEEDINGS ARTICLE | February 4, 2011

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

Proc. SPIE. 7870, Image Processing: Algorithms and Systems IX

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

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PROCEEDINGS ARTICLE | February 4, 2011

Efficiency analysis of DCT-based filters for color image database

Proc. SPIE. 7870, Image Processing: Algorithms and Systems IX

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

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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.

SPIE Journal Paper | January 1, 2011

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

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

JRS Vol. 5 Issue 1

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 | October 23, 2010

Classification of filtered multichannel images

Proc. SPIE. 7830, Image and Signal Processing for Remote Sensing XVI

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

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SPIE Journal Paper | April 1, 2010

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

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

JEI Vol. 19 Issue 02

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

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PROCEEDINGS ARTICLE | February 8, 2010

A method for blind estimation of spatially correlated noise characteristics

Proc. SPIE. 7532, Image Processing: Algorithms and Systems VIII

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

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PROCEEDINGS ARTICLE | September 28, 2009

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

Proc. SPIE. 7477, Image and Signal Processing for Remote Sensing XV

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

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PROCEEDINGS ARTICLE | February 11, 2009

Local adaptive filtering of images corrupted by nonstationary noise

Proc. SPIE. 7245, Image Processing: Algorithms and Systems VII

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

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PROCEEDINGS ARTICLE | October 10, 2008

Compression and classification of noisy multichannel remote sensing images

Proc. SPIE. 7109, Image and Signal Processing for Remote Sensing XIV

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

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PROCEEDINGS ARTICLE | October 10, 2008

Filtering of radar images based on blind evaluation of noise characteristics

Proc. SPIE. 7109, Image and Signal Processing for Remote Sensing XIV

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

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PROCEEDINGS ARTICLE | March 3, 2008

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

Proc. SPIE. 6812, Image Processing: Algorithms and Systems VI

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

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PROCEEDINGS ARTICLE | February 26, 2008

Image filter effectiveness characterization based on HVS

Proc. SPIE. 6814, Computational Imaging VI

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

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PROCEEDINGS ARTICLE | February 28, 2007

Compression of noisy Bayer pattern color filter array images

Proc. SPIE. 6498, Computational Imaging V

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

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PROCEEDINGS ARTICLE | February 27, 2007

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

Proc. SPIE. 6497, Image Processing: Algorithms and Systems V

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

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PROCEEDINGS ARTICLE | February 27, 2007

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

Proc. SPIE. 6497, Image Processing: Algorithms and Systems V

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

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PROCEEDINGS ARTICLE | September 29, 2006

Quasi-optimal compression of noisy optical and radar images

Proc. SPIE. 6365, Image and Signal Processing for Remote Sensing XII

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

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PROCEEDINGS ARTICLE | March 1, 2005

Locally adaptive image filtering based on learning with clustering

Proc. SPIE. 5672, Image Processing: Algorithms and Systems IV

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

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PROCEEDINGS ARTICLE | May 28, 2004

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

Proc. SPIE. 5298, Image Processing: Algorithms and Systems III

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

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PROCEEDINGS ARTICLE | February 5, 2004

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

Proc. SPIE. 5238, Image and Signal Processing for Remote Sensing IX

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

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Among the most important types of information contained in radar and optical images are the textural features. In real life images these features are partially masked by noise that is always present in registered data, basically multiplicative in radar images and additive in optical ones. Thus, one of the basic steps in processing remote sensing data is the filtering of the observed image. However, despite the fact that a lot of filters have been already designed, relatively little attention has been paid to texture preservation properties of noise attenuation methods. Thus, there are the following actual tasks: 1) to analyze the texture preservation properties of different filters; 2) to design image processing methods that are able to preserve texture features simultaneously with effective noise suppression. In this paper, the texture feature preserving characteristics of different filters are examined using a set of texture samples, different noise levels and a set of parame-ters including spatial correlation and higher order statistics. The traditional locally adaptive two-state hard switching filters are modified to the three-state ones where texture is considered as a particular class. For “detection” of texture regions, special, rather simple, classifiers that are based on joint analysis and processing of the two local activity indicators are proposed. The recommendations concerning the parameter setting of the classifiers are given. All this provides an appropriate trade-off of the designed filter properties. It improves the PSNR for entire image in comparison to the component filters used within the three-state local adaptation framework. Local PSNRs for the considered types of image fragments are practically the same or even better than for the filter type recommended for the processing of the corresponding classes. Real life image examples are presented to demonstrate the efficiency of the proposed filter.

PROCEEDINGS ARTICLE | May 28, 2003

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

Proc. SPIE. 5014, Image Processing: Algorithms and Systems II

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

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PROCEEDINGS ARTICLE | March 7, 2003

Automated robust procedure for SAR image filtering and preliminary analysis

Proc. SPIE. 4883, SAR Image Analysis, Modeling, and Techniques V

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

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PROCEEDINGS ARTICLE | May 22, 2002

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

Proc. SPIE. 4667, Image Processing: Algorithms and Systems

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

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PROCEEDINGS ARTICLE | May 8, 2001

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

Proc. SPIE. 4304, Nonlinear Image Processing and Pattern Analysis XII

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

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PROCEEDINGS ARTICLE | April 3, 2000

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

Proc. SPIE. 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV

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

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PROCEEDINGS ARTICLE | March 3, 2000

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

Proc. SPIE. 3961, Nonlinear Image Processing XI

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

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PROCEEDINGS ARTICLE | April 6, 1998

Nonlinear algorithms of isogram extraction from topographic maps

Proc. SPIE. 3304, Nonlinear Image Processing IX

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

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PROCEEDINGS ARTICLE | March 25, 1996

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

Proc. SPIE. 2662, Nonlinear Image Processing VII

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

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Showing 5 of 33 publications

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