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2-D Neighborhood Operator Using A Single Channel A0 Cell Demonstrates Near Ultimate Data Flow Efficiency

A single channel AO cell having a large time bandwidth product can store ten times the image area required for a single calculation of a 9 x 9 neighborhood operator. With proper data sequencing this storage capability of the AO cell can result in nearly one filtered output data point for every input pixel from the image. The image enters the AO cell as a series of columns which are 90 pixels long rather than the 9 pixels required by the desired 9 x 9 convolution filter. Nine such columns which sit side by side in the image fit in tandem in the AO cell. A mask adjoining the AO cell simultaneously selects the desired nine pixels from each of long columns and multiplies by the (mask) filter function. A pulsed laser source illuminating the AO cell and filter mask is collected on a single detector giving a single neighborhood calculation. Each step of the column data through the AO cell gives a new output data point corresponding to the filter mask stepping down the long columns of the image. The result is efficient data flow in two dimensions, first by inserting one whole new column at a time, and second by making the columns much longer than is required by the filter height. This implementation of a large two dimensional convolution filter requires each data point from the image only 1.1 times, on the average, which is within 10% of the ultimate data flow efficiency.