Wavelet-NAFNet for Low-Dose CT Denoising ===== This project implements a lightweight Wavelet-NAFNet model for low-dose CT image denoising. The model follows a residual noise-prediction strategy.

This project implements a custom algorithm for denoising noisy grayscale images using the Discrete Wavelet Transform (DWT) with the Haar wavelet, implemented from scratch in Python. The algorithm ...

To address the noise issue in fiber optic monitoring signals in frozen soil areas, this study employs wavelet denoising techniques to process the fiber optic signals. Since existing parameter choices ...

Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo ...

Abstract: During data acquisition in dc microgrids (DCMGs), high-impedance fault (HiF) signals are often contaminated with various noise sources such as seismic interference, Gaussian or non-Gaussian ...

Noise suppression is a key component in microseismic monitoring technology. Accurate denoising of microseismic signals is crucial for ensuring reliable data for locating mining-related seismic events ...

Abstract: Signal as a noise confined to small scales or high frequency can be removed preferentially from noisy signals if some prior information is available. According to the uncertainty principle, ...

Projects I'd like to share: WaveletNN WaveletNN is Python package that provides neural network modules (PyTorch's nn.Module) for 1D and 2D wavelet transforms. Wavelet analysis is a powerful technic ...