Dr. Mikhail Leontievich Uss Profile

Dr. Mikhail Leontievich Uss

Associate Professor at National Aerospace Univ

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Publications (11)

Proceedings Article | 4 October 2017 Paper

Analysis of signal-dependent sensor noise on JPEG 2000-compressed Sentinel-2 multi-spectral images

M. Uss, B. Vozel, V. Lukin, K. Chehdi

Proceedings Volume 10427, 104270Y (2017) https://doi.org/10.1117/12.2278007

KEYWORDS: Signal to noise ratio, Image compression, Sensors, Remote sensing, Hyperspectral imaging, Interference (communication), Signal processing, Image analysis, Image filtering, Data modeling

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Proceedings Article | 4 October 2017 Paper

Blind estimation of blur in hyperspectral images

Mo Zhang, Benoit Vozel, Kacem Chehdi, Mykhail Uss, Sergey Abramov, Vladimir Lukin

Proceedings Volume 10427, 104270L (2017) https://doi.org/10.1117/12.2278004

KEYWORDS: Point spread functions, Hyperspectral imaging, Error analysis, Image analysis, Remote sensing, Sensors, Data modeling, Sensing systems, Image restoration, Signal to noise ratio

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Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. When no knowledge is available about the degradations present on the original image, blind restoration methods can only be considered. By blind, we mean absolutely no knowledge neither of the blur point spread function (PSF) nor the original latent channel and the noise level. In this study, we address the blind restoration of the degraded channels component-wise, according to a sequential scheme. For each degraded channel, the sequential scheme estimates the blur point spread function (PSF) in a first stage and deconvolves the degraded channel in a second and final stage by means of using the PSF previously estimated. We propose a new component-wise blind method for estimating effectively and accurately the blur point spread function. This method follows recent approaches suggesting the detection, selection and use of sufficiently salient edges in the current processed channel for supporting the regularized blur PSF estimation. Several modifications are beneficially introduced in our work. A new selection of salient edges through thresholding adequately the cumulative distribution of their corresponding gradient magnitudes is introduced. Besides, quasi-automatic and spatially adaptive tuning of the involved regularization parameters is considered. To prove applicability and higher efficiency of the proposed method, we compare it against the method it originates from and four representative edge-sparsifying regularized methods of the literature already assessed in a previous work. Our attention is mainly paid to the objective analysis (via ݈l₁-norm) of the blur PSF error estimation accuracy. The tests are performed on a synthetic hyperspectral image. This synthetic hyperspectral image has been built from various samples from classified areas of a real-life hyperspectral image, in order to benefit from realistic spatial distribution of reference spectral signatures to recover after synthetic degradation. The synthetic hyperspectral image has been successively degraded with eight real blurs taken from the literature, each of a different support size. Conclusions, practical recommendations and perspectives are drawn from the results experimentally obtained.

Proceedings Article | 18 October 2016 Paper

Statistical power of intensity- and feature-based similarity measures for registration of multimodal remote sensing images

M. Uss, B. Vozel, V. Lukin, K. Chehdi

Proceedings Volume 10004, 1000403 (2016) https://doi.org/10.1117/12.2240895

KEYWORDS: Matrices, Visual process modeling, Image registration, Remote sensing, Radar, Interference (communication), Image processing, Samarium, Lawrencium, Motion models

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Proceedings Article | 18 October 2016 Paper

Accuracy assessment of blind and semi-blind restoration methods for hyperspectral images

Mo Zhang, Benoit Vozel, Kacem Chehdi, Mykhail Uss, Sergey Abramov, Vladimir Lukin

Proceedings Volume 10004, 100040P (2016) https://doi.org/10.1117/12.2240950

KEYWORDS: Hyperspectral imaging, Image filtering, Interference (communication), Remote sensing, Point spread functions, Error analysis, Signal to noise ratio, Image analysis, Image restoration, Information operations

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Proceedings Article | 15 October 2015 Paper

Potential accuracy of translation estimation between radar and optical images

M. Uss, B. Vozel, V. Lukin, K. Chehdi

Proceedings Volume 9643, 96430W (2015) https://doi.org/10.1117/12.2194071

KEYWORDS: Earth observing sensors, Image filtering, Californium, Radar, Image registration, Speckle, Signal to noise ratio, Interference (communication), Landsat, Image analysis

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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 | 17 October 2013 Paper

VST-based lossy compression of hyperspectral data for new generation sensors

Alexander Zemliachenko, Ruslan Kozhemiakin, Mykhail Uss, Sergey Abramov, Vladimir Lukin, Benoit Vozel, Kacem Chehdi

Proceedings Volume 8892, 88920L (2013) https://doi.org/10.1117/12.2028415

KEYWORDS: Image compression, Chromium, Interference (communication), Data compression, Sensors, Hyperspectral imaging, Data acquisition, Image filtering, Control systems, Remote sensing

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SPIE Journal Paper | 1 February 2013 Open Access

Image informative maps for component-wise estimating parameters of signal-dependent noise

Mikhail Uss, Benoit Vozel, Vladimir Lukin, Kacem Chehdi

JEI, Vol. 22, Issue 01, 013019, (February 2013) https://doi.org/10.1117/12.10.1117/1.JEI.22.1.013019

KEYWORDS: Interference (communication), Databases, Image processing, Statistical analysis, Calibration, Sensors, Image analysis, CCD image sensors, Volume rendering, Cameras

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SPIE Journal Paper | 28 June 2012

Maximum likelihood estimation of spatially correlated signal-dependent noise in hyperspectral images

Mikhail Uss, Benoit Vozel, Kacem Chehdi, Vladimir Lukin

OE, Vol. 51, Issue 11, 111712, (June 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.11.111712

KEYWORDS: Interference (communication), Hyperspectral imaging, Sensors, 3D modeling, Correlation function, 3D image processing, Image processing, Signal to noise ratio, Data modeling, Motion models

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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 | 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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Showing 5 of 11 publications

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