Wavelet‐Based Feature Extraction for Efficient High‐Resolution Image Classification

Wavelet‐Based Feature Extraction for Efficient High‐Resolution Image Classification

ABSTRACT Convolutional neural networks (CNNs) typically compress high‐resolution images to minimize computational requirements. However, this can lead to loss of information and reduced accuracy in classification tasks. This paper introduces WaveNet, a novel approach for processing high‐resolution i...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Albert Dede, Henry Nunoo‐Mensah, Emmanuel Kofi Akowuah, Kwame Osei Boateng, Prince Ebenezer Adjei, Francisca Adoma Acheampong, Isaac Acquah, Jerry John Kponyo
Aineistotyyppi: Artikkeli
Kieli:English
Julkaistu: Wiley 2025-02-01
Sarja:Engineering Reports
Aiheet:
classification
computational efficiency
high‐resolution image processing
multi‐scale feature extraction
wavelet transform
Linkit:https://doi.org/10.1002/eng2.70027

Internet

https://doi.org/10.1002/eng2.70027

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