A class of linear algebraic restoration filters is derived for the linear degradation model with additive noise. The filters are based on an optimization criterion involving two effects: first, the error in the restored noiseless image compared to the original image, and second, the energy of additive noise passed through the restoration. These two effects can be balanced with a scalar parameter. For both error types, explicit expressions are derived in terms of the parameter. It is shown that by allowing the first error norm to grow slightly, the noise energy may be considerably reduced. This also has a bearing on the analysis of the behavior of other linear parametric restoration filters.

Parametric projection filter for image and signal restoration

Picture processing: 1976

Computer graphics: A keyword-indexed bibliography for the years 1976, 1977, and 1978

Development of restoration filters based on the use of a narrowness measure for the composite point spread function of a blurring-restoration operation that is the ratio of the energy of the composite PSF in a fixed interval around the origin to its total energy, is discussed. For a continuous-continuous model, this leads to composite PSF's that belong to the family of prolate spheroidal wave functions. For a continuous-discrete model, the solutions do not have simple analytical forms but resemble those of the continuous model. Filters of this type have been found to be superior to those with a radius-of-gyration narrowness measure for the control of sidelobes in the case of a Gaussian blurring function and for restoration of images degraded by linear motion blurring. Details of filter construction and examples of image restoration performance are presented.

Image restoration filters based on a 1-0 weighting over the domain of support of the PSF

The images produced by an X-ray computed tomography (CT) system are significantly different from those normally encountered in digital image processing. Among these special properties are: large dynamic range, low spatial resolution, and noise which is significantly correlated from pixel to pixel. These properties present unique problems in the display of the image. We describe some techniques used in CT displays and discuss optimum processing techniques for maximizing resolution for a given noise level.© (1977) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

J.A. Stuller

Papers

Linear resolution enhancement