Advancing Biotechnology Communication: Application of Enhanced Image Restoration and Depth Generation in English Translation Systems

Abstract

The efficacy of traditional English intelligent translation systems in biotechnology settings has been limited, often resulting in inaccuracies that can impede effective communication. To address these challenges, we propose the integration of an enhanced image restoration algorithm and depth generation into the English translation teaching system, tailored for biotechnological applications. This system utilizes advanced hardware, including the TMS320C6678 chip and FPGA processor, to support the sophisticated demands of image processing and data depth generation. Our improved algorithm leverages a comprehensive database specifically designed to enhance the contextual understanding of biotechnological terminologies, facilitating more accurate and relevant translations. Systematic testing of this new setup has demonstrated a significant increase in the accuracy of translations, thereby improving the quality of English instruction and comprehension in biotechnological contexts. The enhanced translation system has shown potential not only in educational settings but also in professional biotechnology environments, where precise communication is critical. The results underscore the value of implementing this advanced technology in biotechnology communication tools, suggesting that its wider adoption could greatly benefit the field by enhancing the clarity and effectiveness of cross-language interactions in global biotechnological collaborations.