Nishant Tripathi*

Department of Computer Science and Engineering, JAIN (Deemed-To-Be University), Bengaluru, Karnataka, India

*Corresponding Author: Nishant Tripathi, Department of Computer Science and Engineering, JAIN (Deemed-To-Be University), Bengaluru, Karnataka, India

Received: July 30, 2025; Published: November 25, 2025

Abstract

This work presents a performance-centric comparative analysis of leading image denoising techniques—including Median Filter, Total Variation, Non-Local Means (NLM), BM3D, and DnCNN—benchmarked against a novel wavelet-based denoising approach. Evaluations were conducted under diverse noise intensities using key metrics: Bit Error Rate (BER), Peak Signal-to-Noise Ratio (PSNR), and Output SNR. The result of the proposed wavelet technique is identified as high SNR restoration of 46.5 dB with 40 dB input SNR, outperforming deep learning and classical ones at all levels. There are slightly higher BER and PSNR but continuous SNR improvement over the whole noise spectrum confirms it is worth preserving strong signals. This is an adaptive thresholding and multiresolution wavelet decomposition-based algorithm, making possible efficient training-free denoising with little computational cost. Its generalization and efficiency make it an excellent candidate for real-time applications in the domains of biomedical imaging, low-illumination vision systems, and energy constrained wireless sensor networks. Future integrated learning-based refinements will improve edge fidelity and error resilience.

Keywords: Image Denoising; Signal Processing; Wavelet Transform; Non-Metaheuristic Algorithm; Signal to Noise Ratio

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Citation

Citation: Nishant Tripathi. “Image Denoising Using Wavelet Transform: A Non-Metaheuristic Signal Processing Method".Acta Scientific Ophthalmology 8.12 (2025): 16-23.

Copyright

Copyright: © 2025 Nishant Tripathi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.