The Delta-variance analysis, has proven to be an efficient and accurate method of characterising the power spectrum of interstellar turbulence. The implementation presently in use, however, has several shortcomings. We propose and test an improved Delta-variance algorithm for two-dimensional data sets, which is applicable to maps with variable error bars and which can be quickly computed in Fourier space. We calibrate the spatial resolution of the Delta-variance spectra. The new Delta-variance algorithm is based on an appropriate filtering of the data in Fourier space. It allows us to distinguish the influence of variable noise from the actual small-scale structure in the maps and it helps for dealing with the boundary problem in non-periodic and/or irregularly bounded maps. We try several wavelets and test their spatial sensitivity using artificial maps with well known structure sizes. It turns out that different wavelets show different strengths with respect to detecting characteristic structures and spectral indices, i.e. different aspects of map structures. As a reasonable universal compromise for the optimum Delta-variance filter, we propose the Mexican-hat filter with a ratio between the diameters of the core and the annulus of 1.5.
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