| A. K. Katsaggelos, "Introduction," in Digital Image Restoration (A. K. Katsaggelos, ed.), ch. 1, pp. 1--20, Springer-Verlag, 1991. |
....since the sinusoid integrates to zero [19] The transformation from the infinite dimension image space (which we did not consider when we posed the problem in a discrete form in equation (2. 7) to the finite dimension measurement space thus inherently results in an ill conditioned matrix A [20, 21]. Paradoxically, the finer the sampling, the better the discrete system represents the continuous system, and thus the worse the conditioning. The condition number of a matrix is defined as max min . Small singular values, or large condition numbers, denote a large sensitivity to noise. ....
A. K. Katsaggelos, "Introduction," in Digital Image Restoration (A. K. Katsaggelos, ed.), ch. 1, pp. 1--20, Springer-Verlag, 1991.
....and the simulation results will be discussed in more detail in the following sections. 2. Image Restoration 2. 1 The Inverse Problem The field of image restoration is generally concerned with the estimation of uncorrupted images from the noisy and blurred images acquired by imaging systems [15] [16] . In this research, we are concerned with the blur caused by diffraction limited, unaberrated optics with incoherent illumination. Optical systems with these characteristics can be modeled as linear, shift invariant (LSI) operators with well defined optical transfer functions (OTF) derived from ....
A. K. Katsaggelos, "Introduction," in Digital Image Restoration, A. K. Katsaggelos, ed. (SpringerVerlag, NY), 1991.
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