Year: 2025
Type: article
Abstract: This paper presents an innovative wavelet transform-based two-stage non-local means algorithm that offers a new approach to image de-noising. It first decomposes the noisy image into four sub-images with different frequencies, which provides a more detailed analysis. Then, it fuses three high frequency sub-images into one high frequency sub-image, which enhances the image quality. The low-frequency sub-image and the fused high frequency sub-image are de-noised using the non-local means algorithm, which is a highly effective and robust de-noising technique. Furthermore, the de-noised low-frequency sub-image and the high frequency sub-image are merged to form the initial de-noised image. Finally, the initial de-noised image is de-noised again using the non-local means algorithm to produce the final de-noised image. The proposed algorithm is able to effectively remove noise while preserving edges and details.
Source: Thermal Science
DOI: 10.2298/tsci2503015m
Authors: Hongjin Ma, Chenxin Zhang, Weiwei Zhang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $161
OpenAlex ID: W4411454939