  In this paper, we present a new interpretation of non-negatively constrained
convolutional coding problems as blind deconvolution problems with spatially
variant point spread function. In this light, we propose an optimization
framework that generalizes our previous work on non-negative group sparsity for
convolutional models. We then link these concepts to source localization
problems that arise in scientific imaging and provide a visual example on an
image derived from data captured by the Hubble telescope.
