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Image reconstruction using symmetric convolution and discrete trigonometric transforms

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Abstract

We demonstrate how the symmetric convolution-multiplication property of the discrete trigonometric transforms can be applied to problems in image reconstruction. This property allows for linear filtering of degraded images by means of point-by-point multiplication in the transform domain of trigonometric transforms. Specifically, in the transform domain of a type II discrete cosine transform, there is an asymptotically optimum energy compaction near d.c. for highly correlated images, which has advantages in reconstructing images with high-frequency noise. The symmetric convolution-multiplication property allows for scalar representations in the transform-domain space of discrete trigonometric transforms for linear reconstruction filters such as the Wiener filter. An analysis of the scalar Wiener filter’s performance in the trigonometric transform domain is given.

© 1998 Optical Society of America

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