Adaptive FPGA NoC-based Architecture for Multispectral Image Correlation

TL;DR

This paper presents an adaptive FPGA-based NoC architecture designed for multispectral image correlation, comparing various distance algorithms to optimize performance based on complexity, precision, and execution time.

Contribution

It introduces a flexible FPGA NoC architecture that supports multiple distance algorithms for multispectral image analysis, with a comparative evaluation of their performance.

Findings

RGB algorithm implementation discussed

Comparison of distance algorithms based on complexity and precision

Adaptive architecture enhances multispectral image correlation

Abstract

An adaptive FPGA architecture based on the NoC (Network-on-Chip) approach is used for the multispectral image correlation. This architecture must contain several distance algorithms depending on the characteristics of spectral images and the precision of the authentication. The analysis of distance algorithms is required which bases on the algorithmic complexity, result precision, execution time and the adaptability of the implementation. This paper presents the comparison of these distance computation algorithms on one spectral database. The result of a RGB algorithm implementation was discussed.