  Natural images follow statistics inherited by the structure of our physical
(visual) environment. In particular, a prominent facet of this structure is
that images can be described by a relatively sparse number of features. We
designed a sparse coding algorithm biologically-inspired by the architecture of
the primary visual cortex. We show here that coefficients of this
representation exhibit a heavy-tailed distribution. For each image, the
parameters of this distribution characterize sparseness and vary from image to
image. To investigate the role of this sparseness, we designed a new class of
random textured stimuli with a controlled sparseness value inspired by our
measurements on natural images. Then, we provide with a method to synthesize
random textures images with a given statistics for sparseness that matches that
of some given class of natural images and provide perspectives for their use in
neurophysiology.
