Dr. Ljubomir Jovanov Profile

Dr. Ljubomir Jovanov

Postdoc at Univ Gent

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Author

Publications (14)

Proceedings Article | 28 May 2024 Presentation + Paper

Enhanced FMCW depth sensing

Ljubomir Jovanov, Wilfried Philips

Proceedings Volume 13083, 130830N (2024) https://doi.org/10.1117/12.3022860

KEYWORDS: Denoising, LIDAR, Distance measurement, Point clouds, Sensors, Convolution, Interferograms, Tunable filters, Modulation frequency, Deconvolution

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Proceedings Article | 9 October 2018 Paper

Image-based end-to-end EO system performance modeling as a design and optimization tool

M. Hogervorst, P. Bijl, P. Fuller, R. Aartsen, L. Jovanov

Proceedings Volume 10795, 107950O (2018) https://doi.org/10.1117/12.2326284

KEYWORDS: Sensors, Signal to noise ratio, Image sensors, Systems modeling, Electro optical modeling, Imaging systems, Image processing, Performance modeling, Modulation transfer functions, Signal processing

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Proceedings Article | 24 May 2018 Presentation + Paper

Real-time spatially adaptive deconvolution for CFA images

Ljubomir Jovanov, Wilfried Philips

Proceedings Volume 10679, 106790X (2018) https://doi.org/10.1117/12.2306110

KEYWORDS: Point spread functions, Deconvolution, Cameras, Chromatic aberrations, Zoom lenses, Image resolution, Optical sensors, Lenses, Imaging systems, Optical filters

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In numerous applications, such as surveillance, industrial inspection, medical imaging and security, high resolution is of crucial importance for the performance of the computer vision systems. Besides spatial resolution, high frame rate is also of high importance in these applications. While the resolution of CMOS imaging sensors is following Moores law, for optics it is becoming increasingly challenging to follow such a development.

In order to follow the pixel size reduction, lenses have to be constructed with much more precision, while the physical size increases dramatically. Moreover, expertise needed to construct a lens of sufficient quality is available at a very few locations in the world. The use of lower quality lenses with high resolution imaging sensors, lead to numerous artifacts.

Due to the different light refraction indexes for different wavelengths, primary color components do not reach their targeted pixels in the sensor plane, which causes lateral chromatic aberration artifacts. These artifacts manifest as false colors in high contrast regions around the edges. Moreover, due to the variable refraction indexes, light rays do not focus on the imaging sensor plain, but in front or behind it, which leads to blur due to the axial aberration. Due to the increased resolution, the size of the pixel is significantly reduced, which reduces the amount of light it receives. As a consequence, the amount of noise increases dramatically. The amount of noise further increases due to the high frame rate and therefore shorter exposure times. In order to reduce the complexity and the price, most cameras today are built using one imaging sensor with spatial color multiplexing filter arrays. This way, camera manufacturers avoid using three imaging sensors and beam splitters, which significantly reduces the price of the system. Since not all color components are present at each pixel location it is necessary to interpolate them, i.e. to perform demosaicking. In the presence of lateral chromatic aberration, this task becomes more complex, since many pixels in the CFA do not receive a proper color, which creates occlusions which in turn create additional artifacts after demosaicing. To prevent this type of artifacts, occlusion inpainting has to be performed.

In this paper we propose a new method for simultaneous correction of all artifacts mentioned above. We define operators representing spatially variable blur, subsampling and noise applied to the unknown artifacts free image, and perform reconstruction of the artifacts free image. First we perform lens calibration step in order to acquire the lens point spread (PSF) function at each pixel in the image using point source. Once we obtained PSFs we perform joint deconvolution using variable kernels obtained from the previous step.

Proceedings Article | 14 May 2018 Presentation + Paper

Real time demosaicking and superresolution of multispectral images

Ljubomir Jovanov, Wilfried Philips

Proceedings Volume 10670, 1067006 (2018) https://doi.org/10.1117/12.2307830

KEYWORDS: Multispectral imaging, Sensors, Near infrared, Super resolution, Video, Reconstruction algorithms

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Proceedings Article | 19 September 2017 Presentation + Paper

Joint denoising, demosaicing, and chromatic aberration correction for UHD video

Ljubomir Jovanov, Wilfried Philips, Klaas Jan Damstra, Frank Ellenbroek

Proceedings Volume 10396, 1039602 (2017) https://doi.org/10.1117/12.2273401

KEYWORDS: Video surveillance, Video, Chromatic aberrations, Denoising, Image resolution, Surveillance, Inspection, Spatial resolution

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Proceedings Article | 27 September 2016 Presentation + Paper

HDR video synthesis for vision systems in dynamic scenes

Ivana Shopovska, Ljubomir Jovanov, Bart Goossens, Wilfried Philips

Proceedings Volume 9971, 99710C (2016) https://doi.org/10.1117/12.2236940

KEYWORDS: High dynamic range imaging, Cameras, Video, Imaging systems, Motion estimation, Sensors, Corner detection, Motion detection, Image processing, Motion models

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SPIE Journal Paper | 16 February 2016

Multiview video quality enhancement

Ljubomir Jovanov, Hiêp Luong, Wilfried Philips

JEI, Vol. 25, Issue 01, 013031, (February 2016) https://doi.org/10.1117/12.10.1117/1.JEI.25.1.013031

KEYWORDS: Video, Cameras, Denoising, Image segmentation, Wavelets, Motion estimation, Visualization, Interference (communication), Video processing, Error analysis

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Proceedings Article | 16 March 2015 Paper

Multiview image sequence enhancement

Ljubomir Jovanov, Hiêp Luong, Tijana Ružic, Wilfried Philips

Proceedings Volume 9399, 93990K (2015) https://doi.org/10.1117/12.2083254

KEYWORDS: Cameras, Denoising, Video, Wavelets, Visualization, Interference (communication), Motion estimation, Image segmentation, Image enhancement, Video processing

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Proceedings Article | 13 November 2014 Paper

Visual quality assessment of H.264/AVC compressed laparoscopic video

Asli Kumcu, Klaas Bombeke, Heng Chen, Ljubomir Jovanov, Ljiljana Platisa, Hiep Luong, Jan Van Looy, Yves Van Nieuwenhove, Peter Schelkens, Wilfried Philips

Proceedings Volume 9037, 90370A (2014) https://doi.org/10.1117/12.2044336

KEYWORDS: Surgery, Video, Video compression, Image compression, Laparoscopy, Image quality, Cameras, Visualization, Quality measurement, Visual compression

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Proceedings Article | 2 September 2009 Paper

Multiresolution example-based depth image restoration

Ljubomir Jovanov, Aleksandra Pižurica, Wilfried Philips

Proceedings Volume 7443, 74431C (2009) https://doi.org/10.1117/12.826226

KEYWORDS: Image resolution, Wavelets, Super resolution, Image filtering, Video, Image sensors, Image processing, Image quality, Sensors, Cameras

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Proceedings Article | 15 September 2008 Paper

Efficient video segmentation using temporally updated mean shift clustering

Nemanja Petrović, Ljubomir Jovanov, Aleksandra Pižurica, Wilfried Philips

Proceedings Volume 7073, 70731R (2008) https://doi.org/10.1117/12.792998

KEYWORDS: Video, Image segmentation, Video processing, Data centers, Data processing, Image processing algorithms and systems, Switches, Semantic video, Human vision and color perception, Video coding

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Proceedings Article | 11 October 2007 Paper

Content adaptive wavelet based method for joint denoising of depth and luminance images

Ljubomir Jovanov, Nemanja Petrovic, Aleksandra Pizurica, Wilfried Philips

Proceedings Volume 6763, 67630D (2007) https://doi.org/10.1117/12.736437

KEYWORDS: Denoising, Wavelets, Image segmentation, Sensors, Interference (communication), Matrices, Image processing algorithms and systems, Data centers, Image filtering, Time of flight cameras

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Proceedings Article | 8 October 2007 Paper

Wavelet-based denoising for 3D OCT images

Vladimir Zlokolica, Ljubomir Jovanov, Aleksandra Pižurica, Paul De Keyser, Frans Dhaenens, Wilfried Philips

Proceedings Volume 6696, 66960P (2007) https://doi.org/10.1117/12.734286

KEYWORDS: Motion estimation, Optical coherence tomography, Wavelets, Denoising, Digital filtering, 3D image processing, Speckle, Reliability, Spatial filters, Image resolution

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Proceedings Article | 20 September 2007 Paper

Wavelet-based stereo images reconstruction using depth images

Ljubomir Jovanov, Aleksandra Pižurica, Wilfried Philips

Proceedings Volume 6701, 67012A (2007) https://doi.org/10.1117/12.734037

KEYWORDS: Motion estimation, Wavelets, Cameras, 3D image processing, Motion models, Video, 3D video streaming, Reconstruction algorithms, Imaging systems, 3D displays

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It is believed by many that three-dimensional (3D) television will be the next logical development toward a more natural and vivid home entertaiment experience. While classical 3D approach requires the transmission of two video streams, one for each view, 3D TV systems based on depth image rendering (DIBR) require a single stream of monoscopic images and a second stream of associated images usually termed depth images or depth maps, that contain per-pixel depth information. Depth map is a two-dimensional function that contains information about distance from camera to a certain point of the object as a function of the image coordinates. By using this depth information and the original image it is possible to reconstruct a virtual image of a nearby viewpoint by projecting the pixels of available image to their locations in 3D space and finding their position in the desired view plane. One of the most significant advantages of the DIBR is that depth maps can be coded more efficiently than two streams corresponding to left and right view of the scene, thereby reducing the bandwidth required for transmission, which makes it possible to reuse existing transmission channels for the transmission of 3D TV. This technique can also be applied for other 3D technologies such as multimedia systems. In this paper we propose an advanced wavelet domain scheme for the reconstruction of stereoscopic images, which solves some of the shortcommings of the existing methods discussed above. We perform the wavelet transform of both the luminance and depth images in order to obtain significant geometric features, which enable more sensible reconstruction of the virtual view. Motion estimation employed in our approach uses Markov random field smoothness prior for regularization of the estimated motion field. The evaluation of the proposed reconstruction method is done on two video sequences which are typically used for comparison of stereo reconstruction algorithms. The results demonstrate advantages of the proposed approach with respect to the state-of-the-art methods, in terms of both objective and subjective performance measures.

Showing 5 of 14 publications

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