# Blue-noise sampling for human retinal cone spatial distribution modeling

## TL;DR

This paper introduces a new Blue-noise sampling method to model human retinal cone distribution, capturing key spatial features and enabling efficient local retinal patch simulations.

## Contribution

The paper presents a novel Blue-noise sampling algorithm tailored for modeling the spatial distribution of human retinal cones, validated against real retinal data.

## Key findings

- The method accurately reproduces cone distribution features.
- It shows comparable results to real retinal data in spatial analysis.
- Efficient for local retinal patch modeling.

## Abstract

This paper proposes a novel method for modeling human retinal cone distribution. It is based on Blue-noise sampling algorithms that share interesting properties with the sampling performed by the mosaic formed by cone photoreceptors in the retina. Here we present the method together with a series of examples of various real retinal patches. The same samples have also been created with alternative algorithms and compared with plots of the center of the inner segments of cone photoreceptors from imaged retinas. Results are evaluated with different distance measure used in the field, like nearest-neighbor analysis and pair correlation function. The proposed method can describe features of a human retinal cone distribution with a certain degree of similarity to the available data and can be efficiently used for modeling local patches of retina.