At the end of the study it is shown that DWT provides appreciable contribution to edge detection results.
📌 3 Key Takeaways
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Objective & Hypothesis:Image processing has crucial effects in many fields like biomedical applications, traffic control, security, satellite systems and so on.
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Key Findings & Data:In this study, we have implemented discrete wavelet transform (DWT) on input images in order to improve accuracy of edge detection applications on ACE16k which is one of the analog processors handling 128x128 images.
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Conclusion & Impact:At the end of the study it is shown that DWT provides appreciable contribution to edge detection results.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
Image processing has crucial effects in many fields like biomedical applications, traffic control, security, satellite systems and so on. Because of its improving importance, various methods are proposed for increasing computation speed and reliability. Cellular Neural Networks - Universal Machine (CNN-UM) is a promising hardware implementa- tion for generating rapid results. In this study, we have implemented discrete wavelet transform (DWT) on input images in order to improve accuracy of edge detection applications on ACE16k which is one of the analog processors handling 128x128 images. Besides, DWT gave us an opportunity to process large-scale images. At the end of the study it is shown that DWT provides appreciable contribution to edge detection results.
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Cite This Article
SEVGEN, S. (2022). Processing large-scale ımages on ace16k using discrete wavelet transform. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 11 - Sayı: 2, 1375–1378. https://ajindex.com/articles/543698
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