Comparative analysis reveals that biorthogonal wavelets optimize noise reduction in images, highlighting trade-offs in thresholding techniques.
Image denoising is a key challenge in the field of image processing, focusing on eliminating undesirable noise while maintaining essential features like edges and textures. This research comparatively analyzed various methods of the Undecimated Wavelet Transform (UWT) for achieving image denoising. The initial section examined the performance of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) utilizing MATLAB, indicating that biorthogonal wavelets provide optimal noise reduction with minimal degradation of detail. The subsequent section investigated various thresholding techniques, specifically SURE, Hybrid, and Universal by calculating their processing times evaluated over four levels of decomposition in LabVIEW. Results demonstrated that SURE exhibits the longest computational duration, particularly at elevated levels of decomposition, whereas the Hybrid approach offered a favorable balance between performance and processing time. Conversely, the Universal thresholding method is identified as the most expedient, proving to be the most efficient at greater levels of wavelet decomposition
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Israa Hashim Latif (2025) studied this question.