1. A method for processing an image, comprising: receiving a sample set of image data generated by transforming and sampling an optical property of an original color image in a spatial basis, wherein the transformation effected is substantially local in the spatial basis and is λ-Capture where λ is 85 or greater; and producing a set of inferred noise-reduced image data, by applying a generalized inverse of said transform to the sample set of image data, wherein the transform respects predetermined spectral constraints, wherein said generalized inverse is computed by defining an inverse problem in the spatial domain, approximating the Poissonian photon shot noise at each photosensitive sensor element with a Gaussian probability distribution function of the same variance, approximating the mean of said Poissonian by computing an approximate solution to said inverse problem, combining said Gaussian probability distribution function with the Gaussian probability distribution function of any additive noise that is present to obtain a combined Gaussian probability distribution function, and using multi-variate normal regression on the combined Gaussian probability distribution function.