Neural Network Image Deconvolution

Abstract

We examine the problem of deconvolving blurred text. This is a task in which there is strong prior knowledge (e.g., font characteristics) that is hard to express computationally. These priors are implicit, however, in mock data for which the true image is known. When trained on such mock data, a neural network is able to learn a solution to the image deconvolution problem which takes advantage of this implicit prior knowledge. Prior knowledge of image positivity can be hard-wired into the functional architecture of the network, but we leave it to the network to learn most of the parameters of the task from the data. We do not need to tell the network about the point spread function, the intrinsic correlation function, or the noise process.

Neural networks have been compared with the optimal linear filter, and with the Bayesian algorithm MemSys, on a variety of problems. The networks, once trained, were faster image reconstructors than MemSys, and had similar performance.