Biotechnology-Driven Biosensing Recognition Technology for Advancing Dynamic Image Visual Communication Design

Abstract

This study explores the application of biosensing recognition technology in advancing the visual communication design of dynamic images, integrating biotechnology-driven optical biosensors and fluorescence scanners to enhance image visualization and processing. Dynamic image processing is achieved through a combination of pattern recognition, image alignment, and image acquisition techniques, enabling high dynamic range image displays via digital image processing. The visual design of dynamic images incorporates spatial transformation models, similarity measurement criteria, parameter optimization, and color design methods that preserve the original image's color fidelity across sequences. Optical biosensors facilitate direct biological response sensing by tagging biological information and processing color images and feature recognition using fiber-optic biosensors. Dimensionality reduction via the PCA algorithm enables efficient tagging and classification of information images. Performance evaluation of four algorithms—RHEO, RNMO, and LOG+RNMO—under varying lighting conditions demonstrates high detection accuracy, with correct detection rates of 97.71%, 98.21%, and 98.04%, respectively, exceeding a 97.5% success rate. This research highlights the transformative potential of biosensing recognition technology in visual communication, providing a biotechnology-inspired framework for integrating biological responses into dynamic image design. The findings offer valuable insights for developing innovative applications in visual media, health communication, and bioinformatics-based visualization systems.