1. A method for reconstructing an object model from a data set containing noise, the method comprising: receiving within a processor the data set defined in a data space, wherein the processor is operable for: constructing an object model in an object space wherein the object model comprises a plurality of object points; devising a transformation of the object model from object space to data space resulting in a data model, such transformation corresponding to the physical process by which the data are actually obtained; selecting a merit function for determining a fit of a data model to the data set; determining an updating variable of the object model in object space based upon the merit function; smoothing the updating variable to determine a smoothed updating variable by: convolving the updating variable with each of a plurality of pixon kernels; and selecting for each object point of the input object a pixon kernel having a largest size that meets a predetermined minimum criteria; generating a pixon map by assigning indices at each object point of the input object corresponding to the selected pixon kernel; and generating an output comprising a substantially denoised object model based on the indices within the pixon map.