Nonlinear image processing by a rotating kernel transformation.

We present a new method for nonlinear image processing that is well suited for hybrid optical-electronic implementation. An input image is convolved with a long, narrow two-dimensional kernel that is rotated, either continuously or discretely, through 360 deg. During rotation the convolution output is monitored, and the maximum and minimum values measured at each point are stored. The processed image is given by an application-dependent function of Max(x, y) and Min(x, y). Setting the output equal to [Max(x, y) - Min(x, y)] enhances straight-line features in noisy, low-contrast images. Better results can be obtained by cascading a Max and a [Max - Min] operation. Numerically calculated examples illustrate the method and compare it with linear filtering.