TFDWT: Fast Discrete Wavelet Transform TensorFlow Layers
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912311283810304 |
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| author | Tarafdar, Kishore K. Gadre, Vikram M. |
| author_facet | Tarafdar, Kishore K. Gadre, Vikram M. |
| contents | TFDWT is an open-source Python library that allows the construction of TensorFlow Layers for Fast Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) in end-to-end backpropagation learning networks. By definition, a multiresolution signal representation using a multi-rate discrete wavelet system creates enriched joint natural-frequency representations. These layers facilitate the construction of multilevel DWT filter banks and Wavelet Packet Transform (WPT) filter banks for a spatial-frequency representation of the inputs and features in shallow or deep networks. The discrete wavelet system partitions the frequency plane into subbands using orthogonal dilated and translated lowpass scaling and highpass wavelet function. The realization of a fast discrete wavelet system is a two-band perfect reconstruction multi-rate filter bank with FIR filters corresponding to the impulse responses of the scaling and wavelet function with downsampling and upsampling operations. A filter bank for a higher dimensional input is a seamless extension by successive separable circular convolutions across each independent axis. The command `pip install TFDWT' installs the latest version of the package. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TFDWT: Fast Discrete Wavelet Transform TensorFlow Layers Tarafdar, Kishore K. Gadre, Vikram M. Signal Processing TFDWT is an open-source Python library that allows the construction of TensorFlow Layers for Fast Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) in end-to-end backpropagation learning networks. By definition, a multiresolution signal representation using a multi-rate discrete wavelet system creates enriched joint natural-frequency representations. These layers facilitate the construction of multilevel DWT filter banks and Wavelet Packet Transform (WPT) filter banks for a spatial-frequency representation of the inputs and features in shallow or deep networks. The discrete wavelet system partitions the frequency plane into subbands using orthogonal dilated and translated lowpass scaling and highpass wavelet function. The realization of a fast discrete wavelet system is a two-band perfect reconstruction multi-rate filter bank with FIR filters corresponding to the impulse responses of the scaling and wavelet function with downsampling and upsampling operations. A filter bank for a higher dimensional input is a seamless extension by successive separable circular convolutions across each independent axis. The command `pip install TFDWT' installs the latest version of the package. |
| title | TFDWT: Fast Discrete Wavelet Transform TensorFlow Layers |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.04168 |