Prof. Ofer Hadar
Communication Systems Engineering Department, Ben-Gurion University, Be’er-Sheva, 8410501, Israel
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Academic Career Length
36Years (1991-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 February 1994 in Optical Engineering
Journal Article
A method of calculating numerically the optical transfer function appropriate to any type of image motion and vibration, including random ones, has been developed. We compare the numerical calculation method to the experimental measurement; the close agreement justifies implementation in image restoration for blurring from any type of image motion. In addition, statistics regarding the limitation of resolution as a function of relative exposure time for low-frequency vibrations involving random blur are described. An analytical approximation to the probability density function for random blur has been obtained. This can be used for the determination of target acquisition probability. A comparison of image quality is presented for three different types of motion: linear, acceleration, and high-frequency vibration for the same blur radius. The parameter considered is the power spectrum of the picture.
1 January 1992 in Optical Engineering
Journal Article
Low-frequency mechanical vibrations are a significant problem in robotics, machine vision, and practical reconnaissance where primary image vibrations involve random process blur radii. They cannot be described by an analytical MTF. A method of numerical calculation of MTF, relevant in principle to any type of image motion, is presented. It is demonstrated here for linear, high, and low vibration frequencies. The method yields the expected closed form solutions for linear and high-frequency motion. The low-vibration-frequency situation involves random process blur radii and MTFs that can only be handled statistically since no closed form solution is possible. This is illustrated here. Comparisons are made to a closed form approximate MTF solution suggested previously for low-frequency motion. Agreement between that analytical approximation and exact MTF calculated numerically is generally good, especially for relatively large and linear motion blur radius situations. For nonlinear short exposure motion, MTF levels off at relatively high nonzero values and never approaches zero. Such situations yield a two-fold benefit: (1) larger spatial frequency bandwidth and (2) higher MTF values at all spatial frequencies since MTF does not approach zero.
15 August 2008 in IEEE Transactions on Broadcasting
Journal Article
RSRon Shmueli
Ron Shmueli
Ofer HadarRHRevital Huber
Revital Huber
MMMasha Maltz
Masha Maltz
MHMerav Huber
Merav Huber
We analyze viewer-perceived quality of a compressed video stream, transmitted over a lossy IP network with a quality of service mechanism. The parameters of the encoding schemes include the transmission bit rate, the compression depth, the frame size and the frame rate. We demonstrate that when jointly considering the impact of the coding bit rate, the packet loss ratio and the video characteristics, we can identify an optimal encoding scheme that maximizes viewer-perceived quality. The video content, the compression and the transmission are represented by a vector X which contains d parameters. Based on subjective tests, we obtain a set of observation pairs of labeled samples Pi = {Xi, Qi} , where Qi is the quality class related to the vector of input parameters Xi. To determine the significance of these results, we use the analysis of variance (ANOVA) statistical method, which identifies those factors that cause differences in the averages in the subjective tests results, and determines the significance of the results. Finally, we introduce a novel method to predict an optimal encoding scheme based on canonical discriminant analysis (CDA) for feature classification.
1 January 1991 in Optical Engineering
Journal Article
A theoretical model, developed by Wulich and Kopeika that gives the MTF for flow vibration frequency sinusoidal image motion applicable to reconnaissance, robotics, and computer vision, is evaluated experimentally to determine (1) accuracy of the MTF model and the validity of assumptions upon which it is based, (2) accuracy of "lucky shot" theoretical analysis to determine the number of independent images required to obtain at least one good quality image, and (3) accuracy of prediction for average blur radius. In most cases agreement between theory and experiment is quite good. Discrepancies are not too great and are attributed to problems with underlying theoretical assumptions where uniform linear motion cannot be assumed. The theory and experiment here are confined to low-frequency sinusoidal vibration where blur radius and spatial frequency content are random processes.
1 October 1996 in Optical Engineering
Journal Article
The restoration of images blurred as a result of image motion or vibration is discussed. The key to the success of the restoration algorithm is accurately determining the optical transfer function (OTF) representing the image motion degradation in the spatial frequency domain. The basic method of obtaining the OTF from the measured function of relative displacement between the camera and the object using a motion sensor has been developed recently and is discussed elsewhere (Sezan and Lagendijk, 1993). The motion function is derived instead from an analysis of a sequence of consecutive images. The first step is to obtain the image motion information from the sequence of images according to two well-known algorithms—the block-matching algorithm (BMA) and the edge trace tracking (ETT). The basis for these two methods consists of tracking a block or an edge through a sequence of several consecutive images. Results of these two methods were fitted to a sinusoidal vibration function and compared; there was excellent agreement between them. Finally, the image is restored using the OTF estimated via the tracking method.
6 February 2024 in Multimedia Tools and Applications
Journal Article
Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represents the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stego-image, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of images and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1.
About Me
Prof. Hadar received the BSc, the MSc (cum laude) and the PhD degrees from the Ben-Gurion University of the Negev, Israel, in 1990, 1992, and 1997, respectively, all in electrical and computer engineering. The prestigious Clore Fellowship supported his PhD studies. His PhD dissertation dealt with the effects of vibrations and motion on image quality and target acquisition. From August 1996 to February 1997, he was with CREOL at Central Florida University, Orlando, FL, as a research visiting scientist. From October 1997 to March 1999, he was a post-doctoral fellow in the Department of Computer Science at the Technion- Israel Institute of Technology, Haifa. In 1999 Prof. Hadar joined the Communication Systems Engineering Department at Ben-Gurion University of the Negev. Currently, he is a Full Professor and was the head of the department between 2012-2018. His research interests include image compression, advanced video coding, H.264, SVC, HECV, transmission of video over IP networks, data hiding and cyber in compressed video streaming. In the last few year Prof. Hadar main research topics involve with Deep Learning in various founded projects by competitive funding agencies, in a variety hot and emerging research areas such as autonomous cars, precision agriculture, tele-medicine. From August 2015 Prof. Hadar to February 2016, he was in Sabbatical as a visiting Professor at Electrical Engineering Dept. at UCLA. Prof. Hadar is a Senior member of IEEE and SPIE.