<title>Wavelet transform-based image processing using acousto-optic correlators</title>

Abstract

Recently there has been a great deal of interest in the use of wavelets and implementation of wavelet transforms. We discuss applications of the wavelet transform to image analysis, including target recognition, rotation and scale invariance, and pattern recognition in the presence of background noise. We propose a method for both scale and rotation invariant pattern recognition based on wavelet features of an image. Wavelets offer advantages in these applications because of their improved ability to discriminate signals in the presence of noise. Performance can be improved by careful selection of the mother wavelet for a given application; we have chosen a two-dimensional Gaussian mother wavelet. Computer simulations of wavelet transform based pattern recognition are discussed, which illustrate scale and rotation invariant target recognition in the presence of noise. Because the wavelet transform is essentially a correlation between the input signal and the family of daughter wavelets, it may be implemented by any type of programmable correlator. In particular, acousto-optic devices offer several advantages; they are programmable and capable of changing their input functions in real time, and commercial devices are available which offer large time-bandwidth products. Acousto-optic image correlators are discussed as a potential implementation of the wavelet transform; by encoding a 1 dimensional wavelet filter bank as a 2 dimensional image, we can implement the wavelet transform image processor without requiring a 2 dimensional spatial light modulator. An alternative implementation utilizes a 2 dimensional array of acousto-optic correlators for a hybrid implementation of a quadrature mirror filter bank.