# Biologically-inspired characterization of sparseness in natural images

## TL;DR

This paper introduces a biologically-inspired sparse coding algorithm for natural images, characterizes sparseness with heavy-tailed distributions, and creates controlled stimuli for neurophysiological studies.

## Contribution

It presents a new sparse coding method inspired by visual cortex architecture and a technique to synthesize textures with specific sparseness statistics.

## Key findings

- Coefficients follow a heavy-tailed distribution.
- Sparseness parameters vary across images.
- Synthesized textures match natural image sparseness.

## Abstract

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.