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 power-law distribution. For each image, the exponent of this distribution characterizes sparseness and varies 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 measurements of natural images. Then, we provide with a method to synthesize random textures images with a given sparseness statistics that match that of some class of natural images and provide perspectives for their use in neurophysiology.