In this paper, we consider application of 2-D stochastic models discussed in [1] to develop noncausal FIR filters for restoration of images degraded by additive white noise. The semicausal model of [1] is used to desi...
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In this paper, we consider application of 2-D stochastic models discussed in [1] to develop noncausal FIR filters for restoration of images degraded by additive white noise. The semicausal model of [1] is used to design masks for edge extraction from the noisy images. The results presented here indicate that good restorations and robust edge detection are possible using relatively simple algorithms.
For several signalprocessing applications, the usefulness of Fast Unitary Transforms (FUT) is now well recognized [1-7]. For signal representation, filtering and encoding, it is well known that the Karhunen-Loeve (KL...
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For several signalprocessing applications, the usefulness of Fast Unitary Transforms (FUT) is now well recognized [1-7]. For signal representation, filtering and encoding, it is well known that the Karhunen-Loeve (KL) Transform, based on signal statistics, is optimum in various senses, but the KL Transform is slow. Suboptimum FUT's allow a trade-off between performance and speed. In this paper, we compare and rank the KL, Fourier, Walsh-Hadamard, Haar, Discrete Cosine, Slant Walsh Hadamard and Slant Haar Transforms by their performance in applications and by the number of elementary operations they require. In encoding and filtering, recursive techniques are widely used and are generally fast. By considering both performance and computations we are able to compare directly recursive and transform algorithms. The comparison brings to light a performance versus computation bound for the two classes of processing techniques.
Several state-of-the-art mathematical models useful in imageprocessing are considered. These models include the traditional fast unitary transforms, autoregessive and state variable models as well as two-dimensional ...
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Several state-of-the-art mathematical models useful in imageprocessing are considered. These models include the traditional fast unitary transforms, autoregessive and state variable models as well as two-dimensional linear prediction models. These models introduced earlier [51], [52] as low-order finite difference approximations of partial differential equations are generalized and extended to higher order in the framework of linear prediction theory. Applications in several imageprocessing problems, including image restoration, smoothing, enhancement, data compression, spectral estimation, and filter design, are discussed and examples given.
images acquired by remote sensing contain radiometric errors caused by variations in the sensor response. In this note, we present a unified treatment of the correction of periodic or nonperiodic errors, which provide...
images acquired by remote sensing contain radiometric errors caused by variations in the sensor response. In this note, we present a unified treatment of the correction of periodic or nonperiodic errors, which provides some insight into the relation of correction algorithms to the type of radiometric degradation. Successful correction of a NOAA Very High Resolution Radiometer (VHRR) thermal infrared image is demonstrated.
The mean-squared-error measure of quality is used as a basis for a general nearest prototype signal-classification methodology. Canonical signal features for this methodology are identified. A consistency requirement ...
The mean-squared-error measure of quality is used as a basis for a general nearest prototype signal-classification methodology. Canonical signal features for this methodology are identified. A consistency requirement is proposed and used to develop a general approach for determining appropriate class prototypes in discriminant space. It is shown that the class indicator, which is a commonly used class prototype in pattern recognition applications, will often violate the consistency requirement. The general results are used to obtain a solution to a previously posed complexity-performance trade-off problem for matched-filter-tapped-delay-line receivers for serial signal classification in an M -ary data transmission system.
Two-stage noise cancellation is a recently introduced technique for cancelling noise while maintaining minimal signal distortion due to signal leakage into the noise reference. An alternative three-stage approach to n...
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Two-stage noise cancellation is a recently introduced technique for cancelling noise while maintaining minimal signal distortion due to signal leakage into the noise reference. An alternative three-stage approach to noise cancellation that extends applicability from intermittent or slowly fluctuating signals to arbitrary signals is presented. This alternative is viable whenever the signal source is accessible so that a pilot signal can be additively embedded.
An approach to the realization of a recursive-like adaptive noise canceller that employs only nonrecursive adaptive filters is presented. This approach is an alternative to prior art that has the advantage of guarante...
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An approach to the realization of a recursive-like adaptive noise canceller that employs only nonrecursive adaptive filters is presented. This approach is an alternative to prior art that has the advantage of guaranteed stability, but the disadvantage of residual signal distortion.
The adaptive adjustment algorithm for a prior art recursive-like noise canceller must incorporate a constraint that prevents convergence to the useless trivial solution. This constraint can also prevent convergence to...
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The adaptive adjustment algorithm for a prior art recursive-like noise canceller must incorporate a constraint that prevents convergence to the useless trivial solution. This constraint can also prevent convergence to the best attainable solution. An alternative to the prior-art hard-constrained recursive-like canceller, that "softens" the constraint by making it adaptive, is proposed.
With the continuing growth of modern communications technology, demand for image transmission and storage is increasing rapidly. Advances in computer technology for mass storage and digital processing have paved the w...
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With the continuing growth of modern communications technology, demand for image transmission and storage is increasing rapidly. Advances in computer technology for mass storage and digital processing have paved the way for implementing advanced data compression techniques to improve the efficiency of transmission and storage of images. In this paper a large variety of algorithms for image data compression are considered. Starting with simple techniques of sampling and pulse code modulation (PCM), state of the art algorithms for two-dimensional data transmission are reviewed. Topics covered include differential PCM (DPCM) and predictive coding, transform coding, hybrid coding, interframe coding, adaptive techniques, and applications. Effects of channel errors and other miscellaneous related topics are also considered. While most of the examples and image models have been specialized for visual images, the techniques discussed here could be easily adapted more generally for multidimensional data compression. Our emphasis here is on fundamentals of the various techniques. A comprehensive bibliography with comments is included for a reader interested in further details of the theoretical and experimental results discussed here.
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