A COMPARISON OF 6 DECONVOLUTION TECHNIQUES

We present results for the comparison of six deconvolution techniques. The methods we consider are based on Fourier transforms, system ident ification, constrained optimization, the use of cubic spline basis fun ctions, maximum entropy, and a genetic algorithm. We compare the perfo rmance of these techniques by applying them to simulated noisy data, i n order to extract an input function when the unit impulse response is known. The simulated data are generated by convolving the known impul se response with each of five different input functions, and then addi ng noise of constant coefficient of variation. Each algorithm was test ed on 500 data sets, and we define error measures in order to compare the performance of the different methods.