An evolutionary blind image deconvolution algorithm through the pseudo-Wigner distribution

This paper describes a new blind deconvolution method implemented by means of an evolutionary algorithm (EA). The EA is designed following a multi-objective optimisation problem approach. The last generation of the EA is assessed by different quality metrics for determining the solution that provides the best performance. It is shown that different restored images can be obtained from a given testing image. The selection of the best result is accomplished though the use of quality metrics. However, the existence of many quality metrics entails a difficult problem for determining the best output. Here, we present a new robust quality metric, based on the use of the local space-frequency information extracted from the Wigner distribution. We empirically compared its performance with other well-known perceptual metrics. In addition to that, a fusion procedure between all candidate restored output images from the EA is also proposed as an alternative to the selection process. The fusion method is also based on the use of this new measure recently developed by the authors with excellent experimental results.