Abstract

A conventional method of restoring blur-degraded image data is inverse filtering. This technique is usually implemented with 32 or 64 image data points contributing, via two Fourier transforms or one convolution, to each restored output point. We show here that actually about seven data points (in a seven-term convolution) suffice for producing about the same resolution and side-lobe characteristics as for inverse filtering. We calculate, by use of a computer search routine, the necessary weights for use in a convolution-type restoring formula of 5, 7, 11, or 15 terms. The criterion for weight selection is a required first-zero position in the output point spread function and, simultaneously, a minimum value of the largest side lobe in the point spread function. The 15-term case is further constrained to have weights selected from the values −1, 0, and +1, only. This defines a method of restoring by addition and subtraction of image values. Experimental results using diffraction-blurred edge data are used to test the methods.

© 1974 Optical Society of America

Space-variant image restoration by coordinate transformations*

Alexander A. Sawchuk
J. Opt. Soc. Am. 64(2) 138-144 (1974)

Trade Off Between Resolution and Noise in Restoration by Superposition of Images

Bahaa E. A. Saleh
Appl. Opt. 13(8) 1833-1838 (1974)

Maximum-likelihood restoration of nonstationary imagery

Martin J. Lahart
J. Opt. Soc. Am. 64(1) 17-22 (1974)

Degrees of freedom of images from point-like-element pupils

F. Gori and G. Guattari
J. Opt. Soc. Am. 64(4) 453-458 (1974)

Digital Image Restoration by Constrained Deconvolution

David Pearce MacAdam
J. Opt. Soc. Am. 60(12) 1617-1627 (1970)