Dr. Vladimir S. Petrovic Profile

Dr. Vladimir S. Petrovic

Honorary Research Fellow

SPIE Involvement:

Author

SPIE Journal Paper | 1 August 2005

Objective evaluation of signal-level image fusion performance

Vladimir Petrovic, Costas Xydeas

OE, Vol. 44, Issue 08, 087003, (August 2005) https://doi.org/10.1117/12.10.1117/1.2009764

KEYWORDS: Image fusion, Atrial fibrillation, Information visualization, Visualization, Information fusion, Discrete wavelet transforms, Image processing, Optical engineering, Target detection, Data modeling

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Proceedings Article | 1 April 2003 Paper

Multilevel image fusion

Vladimir Petrovic

Proceedings Volume 5099, (2003) https://doi.org/10.1117/12.487286

KEYWORDS: Image fusion, Signal processing, Image processing, Information fusion, Image segmentation, Discrete wavelet transforms, Sensors, Image sensors, Visualization, Signal attenuation

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Signal-level image fusion has in recent years established itself as a useful tool for dealing with vast amounts of image data obtained by disparate sensors. In many modern multisensor systems, fusion algorithms significantly reduce the amount of raw data that needs to be presented or processed without loss of information content as well as provide an effective way of information integrations. One of the most useful and widespread applications of signal-level image fusion is for display purposes. Fused images provide the observer with a more reliable and more complete representation of the scene than would be obtained through single sensor display configurations. In recent years, a plethora of algorithms that deal with problem of fusion for display has been proposed. However, almost all are based on relatively basic processing techniques and do not consider information from higher levels of abstraction. As some recent studies have shown this does not always satisfy the complex demands of a human observer and a more subjectively meaningful approach is required. This paper presents a fusion framework based on the idea that subjectively relevant fusion could be achieved in information at higher levels of abstraction such as image edges and image segment boundaries are used to guide the basic signal-level fusion process. Fusion of processed, higher level information to form a blueprint for fusion at signal level and fusion of information from multiple levels of extraction into a single fusion engine are both considered. When tested on two conventional signal-level fusion methodologies, such multi-level fusion structures eliminated undesirable effects such as a fusion artifacts and loss of visually vital information that compromise their usefulness. Images produced by inclusion of multi-level information in the fusion process are clearer and of generally better quality than those obtained through conventional low-level fusion. This is verified through subjective evaluation and established objective fusion performance metrics.

Proceedings Article | 22 March 2001 Paper

Optimizing multiresolution pixel-level image fusion

Vladimir Petrovic, Costas Xydeas

Proceedings Volume 4385, (2001) https://doi.org/10.1117/12.421097

KEYWORDS: Image fusion, Image processing, Discrete wavelet transforms, Information visualization, Atrial fibrillation, Feature selection, Image resolution, Visualization, Image sensors, Computing systems

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A number of pixel level image fusion schemes have been proposed in the past which combine registered input sensor images into a single fused output image. The two general objectives that underpin the operations of these schemes are a) the transfer of all visually important information form input images into a fused image and b) the minimization of undesirable distortions and artifacts which may be generated in the fused image. Fusion is usually achieved by i) the decomposition of input images into representations of their spectral bands and ii) a selection process which transfers information from input bands to yield the required representation of a single fused output image. Furthermore, decomposition is often based on multi-resolution pyramidal representations and the selection process operates on corresponding input image pyramidal levels using selection templates which focus on local spectral characteristics. The performance of such a multi-resolution pixel level image fusion system depends primarily on the actual decomposition and selection algorithms used. Thus for a given decomposition selection arrangement, fusion performance is dependent on the pyramid size (i.e. number of level) and template size. Pyramid and template sizes on the other hand greatly influence the system's computational complexity. This paper is concerned with the performance optimization/characterization of several multi- resolution image fusion schemes, in general and with performance/ complexity trade-offs in particular. Performance is measured using a subjectively meaningful, objective fusion metric which has been proposed recently by authors and which is based on the preservation of image edge information. Thus fusion systems based on derivatives of Gaussian low-pass pyramid and the Discrete Wavelet transform are examined and their performances versus decomposition/selection parameters are defined and compared. The performance/algorithmic complexity results presented for these multi-resolution fusion systems highlight clearly their strengths and weaknesses.

Proceedings Article | 3 April 2000 Paper

Objective pixel-level image fusion performance measure

Costas Xydeas, Vladimir Petrovic

Proceedings Volume 4051, (2000) https://doi.org/10.1117/12.381668

KEYWORDS: Image fusion, Image processing, Information visualization, Visualization, Image quality, Wavelets, Image analysis, Image filtering, Coherence (optics), Computing systems

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Proceedings Article | 12 March 1999 Paper

Cross-band pixel selection in multiresolution image fusion

Vladimir Petrovic, Costas Xydeas

Proceedings Volume 3719, (1999) https://doi.org/10.1117/12.341353

KEYWORDS: Image fusion, Image filtering, Image processing, Electronic filtering, Optical filters, Signal processing, Wavelets, Distortion, Finite impulse response filters, Image resolution

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Conference Committee Involvement (1)

Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications

23 April 2003 | Orlando, Florida, United States

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