Unsharpness and contrast in digitised images

Abstract

The physical quality of a digitised image is affected by non-digital processes in the imaging instrumentation and by digital processes in the computer. The spatial unsharpness depends on instrumental resolution and on the pixel size of the digital array. The image contrast depends on the properties of the imaging system and on the number of grey scales used in the digital sampling process. The possibility is examined of constructing formulae for spatial unsharpness, and for the analogous parameter in the intensity dimension, defined as threshold contrast, in terms of instrumental and digital components. Formulae are developed from simple principles and tested against the observed properties of computer simulated images. The conclusion is that the concepts and the formulae provide a basis for predicting the effects of digital processing on the total unsharpness and contrast of digitised images.

A software method of optimising a radiograph digitising system

“…A second effect is due to the sampling of position coordinates into discrete pixels (Tait 1982), which introduces a slight anisotropy in that the ESF of a diagonal edge is wider than that of a horizontal or vertical edge. However, the main source of possible asymmetry is in the TV camera and digitising circuitry, where the processes that cause smearing along a horizontal scan are different from those that produce unsharpness across the raster scans.…”

“…The ESF has the same width as the PSF, but a different shape and no directly measurable FWHM parameter. However, the two functions can be compared by plotting the PSF with its integral, and a parameter that is equal to the F W H M resolution can be defined in terms of the intensity difference d. It is likely that the total resolution of a digitising system is a mixture of gaussian and triangular point spread functions (Tait 1982) and for both functions the…”

Noise and threshold contrast characteristics of a digital fluorographic system

“…Replacing l / d in equation (1 1) with E ~) ' / * / E : where Z E : represents the combination of unsharpnesses and E , = d gives Faulkner and Moores (1984) have used a similar expression in deriving threshold contrasts in computed tomography. Three sources of unsharpness can be identified which will influence the sampling aperture (in addition to the signal diameter): the imaging system E ~, the eye E ~, and the digitisation of the image E~ due to sampling a region of the image which is composed of a discrete number of pixels (Tait 1982).…”

“…Threshold signal-to-noise ratios of 2-5 have been used by other authors (Rose 1948, Hay andChesters 1976) and an arbitrary value of 3.0 was assumed. Following Faulkner and Moores (1984), the contribution of the eye to the total unsharpness was estimated by assuming a blurring function of angular diameter 1.1 mrad, and the effect of visual assessment of a digitised image, resulting in an effective spread function of standard deviation 0.56p, was allowed for as recommended by Tait (1982). Threshold contrasts were calculated for a range of kerma values and detail diameters and compared with the experimentally obtained values described in 0 5.3.…”