TY - JOUR
TI - Multi-scale wavelet and deep learning integrated adaptive weighted non-local means denoising algorithm
AU - Ma Hongjin
AU - Zhao Jiayu
AU - Zhang Weiwei
JN - Thermal Science
PY - 2026
VL - 30
IS - 2
SP - 1251
EP - 1258
PT - Article
AB - Image denoising is a critical component in numerous disciplines, including  medical imaging, remote sensing, and computer vision. This paper presents  an adaptive weighted non-local means (MAW-NLM) algorithm, which employs  multi-scale wavelet decomposition and deep learning to address the  shortcomings of conventional non-local means in more intricate scenarios.  The initial step in the image decomposition process involves the division of  the image into low- and high-frequency components through the implementation  of multi-scale wavelet decomposition. This decomposition is followed by the  processing of each component individually, with the objective of preserving  edges and fine details with greater efficacy. A deep learning-based  similarity metric, leveraging a convolutional neural network for  high-dimensional feature extraction, replaces the conventional Euclidean  distance, thus enhancing the robustness of the similarity measurement.  Furthermore, the algorithm dynamically adjusts smoothing parameters based  on local gradient and texture complexity, significantly improving its  adaptability. The integration of a fast nearest-neighbor search algorithm  and a two-stage optimization approach has been demonstrated to enhance both  computational efficiency and denoising effectiveness. The experimental  results demonstrate the superiority of MAW-NLM in comparison to traditional  methods.
DO - 10.2298/TSCI2602251M
ER -