Degree
Master of Science
Program
Computer Science
Supervisor
Mahmoud El-Sakka
Abstract
Patch-based denoising algorithms have an effective improvement in the image denoising domain. The Non-Local Means (NLM) algorithm is the most popular patch-based spatial domain denoising algorithm. Many variants of the NLM algorithm have proposed to improve its performance. Weighted Average (WAV) reprojection algorithm is one of the most effective improvements of the NLM denoising algorithm. Contrary to the NLM algorithm, all the pixels in the patch contribute into the averaging process in the WAV reprojection algorithm, which enhances the denoising performance. The key parameters in the WAV reprojection algorithm are kept fixed regardless of the image structure. In this thesis, an improved WAV reprojection algorithm is proposed, where the patch size is assigned adaptively based on the image structure. The image structure is identified using an improved classification method that is based on the structure tensor matrix. The classification result is also utilized to improve the identification of similar patches in the image. The experimental results show that the denoising performance of the proposed method is better than that of the original WAV reprojection algorithm, as well as some other variants of the NLM algorithm.
Recommended Citation
Alsurayhi, Halimah, "An Adaptive Weighted Average (WAV) Reprojection Algorithm for Image Denoising" (2019). Electronic Thesis and Dissertation Repository. 6196.
https://ir.lib.uwo.ca/etd/6196