A neural network for grey level and color correction used in photofinishing

The application of a multilayer perceptron for color and gray level correction in the field of photofinishing is presented. It is shown, that a neural network can improve the overall performance of a state of the art photo printer. The improved correction ability will reduce the number of unsalable pictures and thus lowers the production costs for the photo laboratory. The training experiments were carried out on a database of 30,000 photos using the MUSIC parallel supercomputer. The MUSIC system made it possible, for the first time, to process this large database in a reasonable time.