Publications of year 1981

Articles in journal or book chapters

1. Robert G. Keys. Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(6):1153-1160, December 1981. Keyword(s): Interpolation, Boundary conditions, Convolution, Digital images, Image converters, Image processing, Image sampling, Interpolation, Kernel, Sampling methods, Signal processing algorithms.

   Abstract:

   Cubic convolution interpolation is a new technique for resampling discrete data. It has a number of desirable features which make it useful for image processing. The technique can be performed efficiently on a digital computer. The cubic convolution interpolation function converges uniformly to the function being interpolated as the sampling increment approaches zero. With the appropriate boundary conditions and constraints on the interpolation kernel, it can be shown that the order of accuracy of the cubic convolution method is between that of linear interpolation and that of cubic splines. A one-dimensional interpolation function is derived in this paper. A separable extension of this algorithm to two dimensions is applied to image data.

   
   

   @Article{keysTASSP1981CubicConvolutionInterpolation,
   author = {Keys, Robert G.},
   journal = {IEEE Transactions on Acoustics, Speech, and Signal Processing},
   title = {Cubic convolution interpolation for digital image processing},
   year = {1981},
   issn = {0096-3518},
   month = dec,
   number = {6},
   pages = {1153-1160},
   volume = {29},
   abstract = {Cubic convolution interpolation is a new technique for resampling discrete data. It has a number of desirable features which make it useful for image processing. The technique can be performed efficiently on a digital computer. The cubic convolution interpolation function converges uniformly to the function being interpolated as the sampling increment approaches zero. With the appropriate boundary conditions and constraints on the interpolation kernel, it can be shown that the order of accuracy of the cubic convolution method is between that of linear interpolation and that of cubic splines. A one-dimensional interpolation function is derived in this paper. A separable extension of this algorithm to two dimensions is applied to image data.},
   doi = {10.1109/TASSP.1981.1163711},
   file = {:keysTASSP1981CubicConvolutionInterpolation.pdf:PDF},
   keywords = {Interpolation, Boundary conditions;Convolution;Digital images;Image converters;Image processing;Image sampling;Interpolation;Kernel;Sampling methods;Signal processing algorithms},
   owner = {ofrey},
   pdf = {../../../docs/keysTASSP1981CubicConvolutionInterpolation.pdf},
   
   }
   




2. Jong-Sen Lee. Refined filtering of image noise using local statistics. Computer Graphics and Image Processing, 15(4):380-389, 1981. Keyword(s): Speckle Filter, Refined Lee filter, Speckle, Synthetic aperture radar, SAR.

   Abstract:

   An effective algorithm for digital image noise filtering is presented in this paper. Most noise filtering techniques such as Kalman filter and transform domain methods require extensive image modeling and produce filtered images with considerable contrast loss. The algorithm proposed in this paper is an extension of Lee's local statistics method modified to utilize local gradient information. It does not require image modeling, and it will not smear edges and subtle details. For both the additive and multiplicative noise cases, the local mean and variance are computed from a reduced set of pixels depending on the orientation of the edge. Consequently, noise along the edge is removed, and the sharpness of the edge is enhanced. For practical applications when the noise variance is spatially varying and unknown, an adaptive filtering algorithm is developed. Experiments show its good potential for processing real-life images. Examples on images containing 256�256 pixels are given to substantiate the theoretical development.

   
   

   @Article{leeCGIP1981RefinedFilteringOfImageNoiseUsingLocalStatistics,
   author = {Jong-Sen Lee},
   journal = {Computer Graphics and Image Processing},
   title = {Refined filtering of image noise using local statistics},
   year = {1981},
   issn = {0146-664X},
   number = {4},
   pages = {380-389},
   volume = {15},
   abstract = {An effective algorithm for digital image noise filtering is presented in this paper. Most noise filtering techniques such as Kalman filter and transform domain methods require extensive image modeling and produce filtered images with considerable contrast loss. The algorithm proposed in this paper is an extension of Lee's local statistics method modified to utilize local gradient information. It does not require image modeling, and it will not smear edges and subtle details. For both the additive and multiplicative noise cases, the local mean and variance are computed from a reduced set of pixels depending on the orientation of the edge. Consequently, noise along the edge is removed, and the sharpness of the edge is enhanced. For practical applications when the noise variance is spatially varying and unknown, an adaptive filtering algorithm is developed. Experiments show its good potential for processing real-life images. Examples on images containing 256�256 pixels are given to substantiate the theoretical development.},
   doi = {https://doi.org/10.1016/S0146-664X(81)80018-4},
   file = {:leeCGIP1981RefinedFilteringOfImageNoiseUsingLocalStatistics.pdf:PDF},
   keywords = {Speckle Filter, Refined Lee filter, Speckle, Synthetic aperture radar, SAR},
   owner = {ofrey},
   url = {https://www.sciencedirect.com/science/article/pii/S0146664X81800184},
   
   }
   

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This collection of SAR literature is far from being complete.
It is rather a collection of papers which I store in my literature data base. Hence, the list of publications under PUBLICATIONS OF AUTHOR'S NAME should NOT be mistaken for a complete bibliography of that author.

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