Use wavelet analysis and other algorithms for the process of image denoising in the mathematical field

Abstract

By using sophisticated mathematical methods in MATLAB, the investigation into the complex field of image noise reduction presented in this paper is thorough. like threshold denoising, correlation denoising, and modal maxima denoising. The use of both manual questionnaires and machine-based evaluation measures to fully evaluate the effectiveness of the denoising algorithms is a key component of this study. This dual strategy, which takes into account both objective and subjective components of image quality enhancement, ensures a well-rounded review. According to our research, correlation denoising consistently proves to be the most practical and effective method across a range of noise types and image categories, although modal maxima denoising and threshold denoising show promising outcomes in some situations. The thorough statistical analysis of our findings supports this conclusion, making it a convincing option for real-world picture denoising applications. In conclusion, this research broadens the range of image denoising techniques, which benefits both the field of image processing and the mathematical community. This study provides a holistic view of image denoising by incorporating wavelet analysis and other sophisticated algorithms, ultimately giving practitioners a deeper understanding of the techniques available for improving image quality in the presence of noise.