Biorthogonal Lattice Tunable Wavelet Units and Their Implementation in Convolutional Neural Networks for Computer Vision Problems
This work introduces a universal wavelet unit constructed with a biorthogonal lattice structure which is a novel tunable wavelet unit to enhance image classification and anomaly detection in convolutional neural networks by reducing information loss during pooling. The unit employs a biorthogonal la...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Signal Processing |
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| Online Access: | https://ieeexplore.ieee.org/document/11039659/ |
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| author | An D. Le Shiwei Jin Sungbal Seo You-Suk Bae Truong Q. Nguyen |
| author_facet | An D. Le Shiwei Jin Sungbal Seo You-Suk Bae Truong Q. Nguyen |
| author_sort | An D. Le |
| collection | DOAJ |
| description | This work introduces a universal wavelet unit constructed with a biorthogonal lattice structure which is a novel tunable wavelet unit to enhance image classification and anomaly detection in convolutional neural networks by reducing information loss during pooling. The unit employs a biorthogonal lattice structure to modify convolution, pooling, and down-sampling operations. Implemented in residual neural networks with 18 layers, it improved detection accuracy on CIFAR10 (by 2.67% ), ImageNet1K (by 1.85% ), and the Describable Textures dataset (by 11.81% ), showcasing its advantages in detecting detailed features. Similar gains are achieved in the implementations for residual neural networks with 34 layers and 50 layers. For anomaly detection on the MVTec Anomaly Detection and TUKPCB datasets, the proposed method achieved a competitive performance and better anomaly localization. |
| format | Article |
| id | doaj-art-08be3afbe7b848a3824aed6ffc28b1af |
| institution | Kabale University |
| issn | 2644-1322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Signal Processing |
| spelling | doaj-art-08be3afbe7b848a3824aed6ffc28b1af2025-08-20T03:50:45ZengIEEEIEEE Open Journal of Signal Processing2644-13222025-01-01676878310.1109/OJSP.2025.358096711039659Biorthogonal Lattice Tunable Wavelet Units and Their Implementation in Convolutional Neural Networks for Computer Vision ProblemsAn D. Le0https://orcid.org/0009-0000-4684-715XShiwei Jin1Sungbal Seo2https://orcid.org/0009-0000-4226-0327You-Suk Bae3Truong Q. Nguyen4Electrical and Computer Engineering Department, University of California San Diego, La Jolla, CA, USAElectrical and Computer Engineering Department, University of California San Diego, La Jolla, CA, USADepartment of Computer Engineering, Tech University of Korea, Siheung, South KoreaDepartment of Computer Engineering, Tech University of Korea, Siheung, South KoreaElectrical and Computer Engineering Department, University of California San Diego, La Jolla, CA, USAThis work introduces a universal wavelet unit constructed with a biorthogonal lattice structure which is a novel tunable wavelet unit to enhance image classification and anomaly detection in convolutional neural networks by reducing information loss during pooling. The unit employs a biorthogonal lattice structure to modify convolution, pooling, and down-sampling operations. Implemented in residual neural networks with 18 layers, it improved detection accuracy on CIFAR10 (by 2.67% ), ImageNet1K (by 1.85% ), and the Describable Textures dataset (by 11.81% ), showcasing its advantages in detecting detailed features. Similar gains are achieved in the implementations for residual neural networks with 34 layers and 50 layers. For anomaly detection on the MVTec Anomaly Detection and TUKPCB datasets, the proposed method achieved a competitive performance and better anomaly localization.https://ieeexplore.ieee.org/document/11039659/Anomaly detectioncomputer visiondiscrete wavelet transformsfeature extractionimage processingimage recognition |
| spellingShingle | An D. Le Shiwei Jin Sungbal Seo You-Suk Bae Truong Q. Nguyen Biorthogonal Lattice Tunable Wavelet Units and Their Implementation in Convolutional Neural Networks for Computer Vision Problems IEEE Open Journal of Signal Processing Anomaly detection computer vision discrete wavelet transforms feature extraction image processing image recognition |
| title | Biorthogonal Lattice Tunable Wavelet Units and Their Implementation in Convolutional Neural Networks for Computer Vision Problems |
| title_full | Biorthogonal Lattice Tunable Wavelet Units and Their Implementation in Convolutional Neural Networks for Computer Vision Problems |
| title_fullStr | Biorthogonal Lattice Tunable Wavelet Units and Their Implementation in Convolutional Neural Networks for Computer Vision Problems |
| title_full_unstemmed | Biorthogonal Lattice Tunable Wavelet Units and Their Implementation in Convolutional Neural Networks for Computer Vision Problems |
| title_short | Biorthogonal Lattice Tunable Wavelet Units and Their Implementation in Convolutional Neural Networks for Computer Vision Problems |
| title_sort | biorthogonal lattice tunable wavelet units and their implementation in convolutional neural networks for computer vision problems |
| topic | Anomaly detection computer vision discrete wavelet transforms feature extraction image processing image recognition |
| url | https://ieeexplore.ieee.org/document/11039659/ |
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