To browse Academia. Log in with Facebook Log in with Google. Remember me on this computer. Enter the email address you signed up with and we'll email you a reset link. Need an account? Click here to sign up. Download Free PDF. Parallel processing architectures for iterative image restoration A short summary of this paper. Download Download PDF. Translate PDF.
IC Katsaggelos, S. A n efficient implementation iterative algorithm derived from the single step regularized iterative may necessitate altering the mathematical structure of the restoration algorithm.
One processor is assigned lo each picture algorithm. We derive and implement a multistep iterative element, with local memory depending on the support of the res- image restoration algorithm, which is characterized by local- toration filter. In Sec. I1 its computation time. VLSI implementations to be con- I. Finally, in Sec. IV conclusions and current research directions are The recovery o r restoration of an image that has been dis- described.
Iterative restoration algorithms will Iterative restoration A n appropriate mathematical model of the image distorting tasks are generally computationally extensive and time con- process is the following [ I ] suming, as is the case with most image processing tasks. There has been a natural interest in improving the response times of the image processors to extend the horizon of their applicability, While early research in this direction focussed on exploiting the structure of the computation o n a single where the vectors y , z and U represent respectively lexico- processor e.
The matrix D represents the space invariant o r Several image processing systems with multiprocessors, such space varying deterministic distortion. Recent technolo-gical revolution, criterion, given y and D. This results in obtaining a restored signalfpicture processing.
Exploitation of a multiprocessor or system requires a radical departure from the traditional Von Neumann environment. In VLSI there are a group of processors, each with a local memory, cooperating where denotes the transpose of a vector or matrix and a, to solve a given problem. The new challenge is to exploit the regularization parameter, is inversely proportional to the properties of VLSI to built etTective and efficient computing signal to noise ratio SNR.
The matrix C represents a structures. The fundamental criteria of optimality are A , the high-pass filter which is chosen in such a way so that the area of the VLSI chip, and T,its computation time. The aim energy of the restored image at high frequencies due pri- is to design architectures that use these two resources in an marily to the noise amplification is bounded Hayes focuses on the understanding of the basic principles while avoiding overemphasis on the arcane aspects of design.
This approach best meets the needs of undergraduate or beginning graduate-level students. Author : B. Supported by solved problems, case studies, and examples, it provides a complete description of computer architecture for professionals ranging from beginners to experienced ones. Salient Features in the revised edition:- Comprehensive coverage of concepts Revised and enhanced review questions Modifications in the chapters according to the latest developments B Govindarajulu is currently working as a faculty at Rajalakshmi Engineering College, Chennai.
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Introduction to parallel processing; Memory and input-output subsystems; Principles of pipelining and vector processing; Pipeline computers and vectorization methods; Structures and algorithms for array processors For print-disabled …. Structures for parallel processing. Parallel processing has been an important concern of the computer engineer for many years. It is employed to achieve high performance by replication or a unique function by interconnecting different … Expand.
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A survey on current high performance parallel computer architectures is given. The focus is on the performance bottlenecks of the different architectures.
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