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Signal Processing Magazine, IEEE

Issue 3 • Date May 2003

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Displaying Results 1 - 12 of 12
  • IEEE Signal Processing Society presents awards

    Publication Year: 2003 , Page(s): 9 - 10
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    Freely Available from IEEE
  • Unexpected paths

    Publication Year: 2003 , Page(s): 11 - 13
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  • Making new stuff work

    Publication Year: 2003 , Page(s): 14 - 18
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  • Super-resolution image reconstruction

    Publication Year: 2003 , Page(s): 19 - 20
    Cited by:  Papers (16)  |  Patents (3)
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  • How I learned to love the trellis

    Publication Year: 2003 , Page(s): 87 - 102
    Cited by:  Papers (3)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (921 KB) |  | HTML iconHTML  

    Shows how the Viterbi decoding algorithm (VDA) can be used for signal equalization and detection in a way that is quite different from the usual equalization approach of adjusting a received signal via filtering to "reverse" the distortion. With the VDA, the receiver "adjusts itself" so as to make good data estimates from a distorted sequence of waveforms. The main goal of this article has been to provide insight as to how the VDA works and why it is a useful tool for equalizing and detecting signals that can be modeled as outputs from an FSM. View full abstract»

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  • Multi-objective super resolution: concepts and examples

    Publication Year: 2003 , Page(s): 49 - 61
    Cited by:  Papers (36)  |  Patents (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (971 KB) |  | HTML iconHTML  

    Described methods for simultaneously generating the super-resolved depth map and the image from LR observations. Structural information is embedded within the observations and, through the two formulations of DFD and SFS problems, we were able to generate the super-resolved images and the structures. The first method described here avoids correspondence and warping problems inherent in current SR techniques involving the motion cue in the LR observations and uses a more natural depth-related defocus as a natural cue in real aperture imaging. The second method, while again avoiding the correspondence problems, also demonstrates the usefulness of the generalized interpolation scheme leading to more flexibility in the final SR image, in the sense that the LR image can be viewed at SR with an arbitrary light source position. The quality of the super-resolved depth and intensity maps has been found to be quite good. The MAP-MRF framework that was used in both methods models both the surface normal and the intensity field as separate MRFs, and this helps in regularizing the solution. View full abstract»

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  • Recursive discrete-time sinusoidal oscillators

    Publication Year: 2003 , Page(s): 103 - 111
    Cited by:  Papers (25)  |  Patents (1)
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    Explored the basic theory of recursive digital oscillators with a bent towards the practical, and from there looked at some common oscillators. Then the article added some mechanisms for controlling their amplitude and adjusting their frequency. A brief example of how these oscillators and their control mechanisms may be used to make FSK modulators is given. View full abstract»

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  • Computer vision applied to super resolution

    Publication Year: 2003 , Page(s): 75 - 86
    Cited by:  Papers (77)  |  Patents (21)
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    Super-resolution (SR) restoration aims to solve the following problem: given a set of observed images, estimate an image at a higher resolution than is present in any of the individual images. Where the application of this technique differs in computer vision from other fields is in the variety and severity of the registration transformation between the images. In particular this transformation is generally unknown, and a significant component of solving the SR problem in computer vision is the estimation of the transformation. The transformation may have a simple parametric form, or it may be scene dependent and have to be estimated for every point. In either case the transformation is estimated directly and automatically from the images. We describe the two key components that are necessary for successful SR restoration: the accurate alignment or registration of the LR images and the formulation of an SR estimator that uses a generative image model together with a prior model of the super-resolved image itself. As with many other problems in computer vision, these different aspects are tackled in a robust, statistical framework. View full abstract»

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  • Super-resolution image reconstruction: a technical overview

    Publication Year: 2003 , Page(s): 21 - 36
    Cited by:  Papers (736)  |  Patents (124)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2720 KB) |  | HTML iconHTML  

    A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an high-resolution (HR) image (or sequence) from observed multiple low-resolution (LR) images. Such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement. In this article, we use the term "SR image reconstruction" to refer to a signal processing approach toward resolution enhancement because the term "super" in "super resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of LR imaging systems. The major advantage of the signal processing approach is that it may cost less and the existing LR imaging systems can be still utilized. The SR image reconstruction is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications. The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts. To this purpose, we present the technical review of various existing SR methodologies which are often employed. Before presenting the review of existing SR algorithms, we first model the LR image acquisition process. View full abstract»

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  • On "Dirty-Paper coding"

    Publication Year: 2003 , Page(s): 112 - 113
    Cited by:  Papers (12)  |  Patents (2)
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    Dirty-paper coding makes an analogy to the problem of writing on dirty paper, where the reader cannot nominally distinguish dirt from ink. There are many scenarios where this result may be applied. In the field of information hiding (or watermarking), theoretical bounds as well as practical watermarking schemes have been found. Another important application of dirty-paper coding is for a multiuser channel wherein a multiple-antenna transmitter is communicating with multiple users. View full abstract»

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  • High-resolution images from low-resolution compressed video

    Publication Year: 2003 , Page(s): 37 - 48
    Cited by:  Papers (44)  |  Patents (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2081 KB) |  | HTML iconHTML  

    Surveys the field of super resolution (SR) processing for compressed video. The introduction of motion vectors, compression noise, and additional redundancies within the image sequence makes this problem fertile ground for novel processing methods. In conducting this survey, though, we develop and present all techniques within the Bayesian framework. This adds consistency to the presentation and facilitates comparison between the different methods. The article is organized as follows. We define the acquisition system utilized by the surveyed procedures. Then we formulate the HR problem within the Bayesian framework and survey models for the acquisition and compression systems. This requires consideration of both the motion vectors and transform coefficients within the compressed bit stream. We survey models for the original HR image intensities and displacement values. We discuss solutions for the SR problem and provide examples of several approaches. View full abstract»

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  • Mathematical analysis of super-resolution methodology

    Publication Year: 2003 , Page(s): 62 - 74
    Cited by:  Papers (62)  |  Patents (32)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (842 KB) |  | HTML iconHTML  

    The attainment of super resolution (SR) from a sequence of degraded undersampled images could be viewed as reconstruction of the high-resolution (HR) image from a finite set of its projections on a sampling lattice. This can then be formulated as an optimization problem whose solution is obtained by minimizing a cost function. The approaches adopted and their analysis to solve the formulated optimization problem are crucial, The image acquisition scheme is important in the modeling of the degradation process. The need for model accuracy is undeniable in the attainment of SR along with the design of the algorithm whose robust implementation will produce the desired quality in the presence of model parameter uncertainty. To keep the presentation focused and of reasonable size, data acquisition with multisensors instead of, say a video camera is considered. View full abstract»

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Aims & Scope

IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Min Wu
University of Maryland, College Park
United States 

http://www/ece.umd.edu/~minwu/