Farah Torkamani-Azar

Bio: Farah Torkamani-Azar is an academic researcher from Shahid Beheshti University. The author has contributed to research in topics: Digital watermarking & Singular value decomposition. The author has an hindex of 9, co-authored 38 publications receiving 409 citations. Previous affiliations of Farah Torkamani-Azar include University of New South Wales.

A Low Complexity Video Watermarking in H.264 Compressed Domain

Abstract: In this paper, a new blind and readable H.264 compressed domain watermarking scheme is proposed in which the embedding/extracting is performed using the syntactic elements of the compressed bit stream. As a result, it is not necessary to fully decode a compressed video stream both in the embedding and extracting processes. The method also presents an inexpensive spatiotemporal analysis that selects the appropriate submacroblocks for embedding, increasing watermark robustness while reducing its impact on visual quality. Meanwhile, the proposed method prevents bit-rate increase and restricts it within an acceptable limit by selecting appropriate quantized residuals for watermark insertion. Regarding watermarking demands such as imperceptibility, bit-rate control, and appropriate level of security, a priority matrix is defined which can be adjusted based on the application requirements. The resulted flexibility expands the usability of the proposed method.

Image recovery using the anisotropic diffusion equation

Abstract: A new approach for image recovery using the anisotropic diffusion equation is developed which is based on the first derivative of the signal in time embedded in family of images with different scales. The diffusion coefficient is determined as a function of the gradient of the signal convolved with a symmetric exponential filter. A new discrete realization is developed for the simultaneous removal of noise and preservation of edges.

Image quality assessment using the singular value decomposition theorem

Abstract: In objective image quality metrics, one of the most important factors is the correlation of their results with the perceived quality measurements. In this paper, a new method is presented based on comparing between the structural properties of the two compared images. Based on the mathematical concept of the singular value decomposition (SVD) theorem, each matrix can be factorized to the products of three matrices, one of them related to the luminance value while the two others show the structural content information of the image. A new method to quantify the quality of images is proposed based on the projected coefficients and the left singular vector matrix of the disturbed image based on the right singular vector matrix of the original image. To evaluate this performance, many tests have been done using a widespread subjective study involving 779 images of the Live Image Quality Assessment Database, Release 2005. The objective results show a high rate of correlation with subjective quality measurements.

Image quality measurement besides distortion type classifying

Abstract: To identify the distortion type and quantify the quality of images, a new method is presented based on a comparison among the structural properties as well as consideration of the luminance characteristics of the two compared images. To fulfill this aim, the mathematical concept of the singular value decomposition (SVD) theorem has been applied. The difference vector of the reflection coefficients of the disturbed and the original image on the right singular vector matrix of the original image are considered. Many tests were conducted to evaluate the performance, using a widespread subjective study involving 779 images from the Live Image Quality Assessment Database, Release 2005. The results showed a greatly improved performance along with the ability to distinguish distortion type of images.

Sparsity-aware adaptive block-based compressive sensing

Abstract: Conventional methods for block-based compressive sensing consider an equal number of samples for all blocks. However, the sparsity order of blocks in natural images could be different and, therefore, a various number of samples could be required for their reconstruction. In this study, the authors propose an adaptive block-based compressive sensing scheme, which collects a different number of samples from each block. The authors show that by adapting the sampling rate, in addition to reducing the whole required number of measurements, the reconstruction performance would be improved, simultaneously. Simulation results verify the effectiveness of the proposed scheme, especially for multi-level pixel value images like Mondrian test image.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Anisotropic diffusion in image processing

Abstract: Preface Through many centuries physics has been one of the most fruitful sources of inspiration for mathematics. As a consequence, mathematics has become an economic language providing a few basic principles which allow to explain a large variety of physical phenomena. Many of them are described in terms of partial diierential equations (PDEs). In recent years, however, mathematics also has been stimulated by other novel elds such as image processing. Goals like image segmentation, multiscale image representation, or image restoration cause a lot of challenging mathematical questions. Nevertheless, these problems frequently have been tackled with a pool of heuristical recipes. Since the treatment of digital images requires very much computing power, these methods had to be fairly simple. With the tremendous advances in computer technology in the last decade, it has become possible to apply more sophisticated techniques such as PDE-based methods which have been inspired by physical processes. Among these techniques, parabolic PDEs have found a lot of attention for smoothing and restoration purposes, see e.g. 113]. To restore images these equations frequently arise from gradient descent methods applied to variational problems. Image smoothing by parabolic PDEs is closely related to the scale-space concept where one embeds the original image into a family of subsequently simpler , more global representations of it. This idea plays a fundamental role for extracting semantically important information. The pioneering work of Alvarez, Guichard, Lions and Morel 11] has demonstrated that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition. Within this framework, two classes can be justiied in a rigorous way as scale-spaces: the linear diiusion equation with constant dif-fusivity and nonlinear so-called morphological PDEs. All these methods satisfy a monotony axiom as smoothing requirement which states that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution. An interesting class of parabolic equations which pursue both scale-space and restoration intentions is given by nonlinear diiusion lters. Methods of this type have been proposed for the rst time by Perona and Malik in 1987 190]. In v vi PREFACE order to smooth the image and to simultaneously enhance semantically important features such as edges, they apply a diiusion process whose diiusivity is steered by local image properties. These lters are diicult to analyse mathematically , as they may act locally like a backward diiusion process. …

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Abstract: Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge.

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Abstract: Compressive sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and cognitive radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the under-utilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency, and space. Such a sparse nature of the spectrum occupancy has inspired the application of CS in CR communications. In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains. In this direction, this survey paper provides a detailed review of the state of the art related to the application of CS in CR communications. Starting with the basic principles and the main features of CS, it provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR. Subsequently, we review the existing CS-related works applied to different categories such as wideband sensing, signal parameter estimation and radio environment map (REM) construction, highlighting the main benefits and the related issues. Furthermore, we present a generalized framework for constructing the REM in compressive settings. Finally, we conclude this survey paper with some suggested open research challenges and future directions.