Deep learning for efficient high-resolution image processing: A systematic review

Résumé fourni par la source

High-resolution images are increasingly used in fields such as remote sensing, medical imaging, and agriculture, but they present significant computational challenges when processed with deep learning models. This paper provides a systematic review of deep learning techniques developed to improve the efficiency of high-resolution image processing. We investigate techniques like lightweight neural networks, vision transformers adapted for high-resolution inputs, and models using frequency-domain inputs based on 96 studies from 2018 to 2023. These techniques have many applications, from environmental monitoring and urban planning to disease diagnosis. We emphasize the advancements in efficient high-resolution deep learning models, discussing their performance in terms of accuracy, speed, and resource requirements. Key challenges, including the trade-off between processing efficiency and model accuracy, are analysed, and potential future research directions are proposed to address these issues. • The paper reviews recent studies on deep learning methods for high-resolution inputs. • Various techniques are examined in terms of computational efficiency. • The review identifies trade-offs between processing efficiency and model accuracy.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.