Bio-inspired image enhancement derived from a 'rank order coding' model
Résumé
In this study, the authors propose a new method to enhance image information, based on wavelet decomposition and original partial reconstruction of image. This reconstruction called 'asynchronous reconstruction' is not carried out in the same way as the usual sequential one. It is based on rank order coding. In fact, while sequential reconstruction is to sum all or a part of the responses obtained for each scale of 'coarse to fine' decomposition, asynchronous reconstruction tries to be closer to human brain which uses a limited number of frequency channels. Actually, after wavelet decomposition, responses are sorted from top down for each pixel of the image. Final asynchronous reconstruction for each pixel is obtained by adding a chosen number of wavelet responses, beginning by the maximum response. So, at a given level of reconstruction, the pixel values do not come from the same frequency channels. The interest of this method has been tested on a face verification task using the IV2 biometric database. Stopping criterion for reconstruction can be a constant number of wavelet responses to use, but an adaptive process has been also investigated. Three criteria are explored: standard deviation, entropy and lost edges ratio.