Neural network for detection and correction of local boundary

Abstract

A network is provided for the detection and correction of local boundary misalignments in a two-dimensional pixel space between a reference and transformed image. An input layer has input layer sections, each of which contains a plurality of input nodes associated with a cell. The cell is centered on a pixel and divided along a straightline orientation into first and second cell sections. Each of the input nodes outputs a digital signal indicative of the presence or absence of a contrast gradient as measured by the two cell sections. A second layer has a plurality of second layer sections, each of which is associated with one of the input layer sections and contains a plurality of second layer nodes. Each second layer node is responsive to a combination of input nodes to indicate the presence or absence of a boundary misalignment between the reference and transformed images. Presence of a contrast gradient at the combination of nodes defines a local boundary misalignment. A third layer has a plurality of third layer nodes, each of which is associated with one of the second layer sections. Each third layer node weights and combines outputs of the second layer nodes to output a signal defining a direction to shift the transformed image perpendicular to the straightline orientation. The third layer are outputs a signal defining the local correction of the local boundary misalignment between the reference and transformed images for the centered pixel.