Image Denoising for Gaussian Noise Reduction in Bionics Using DWT Technique

Abstract

The presence of Gaussian noise in bio-medical images, particularly those used in bionic applications (such as prosthetic vision, neural interfaces, or medical diagnostics), significantly degrades image quality, hindering accurate analysis, interpretation, and subsequent device performance. This paper proposes an effective image denoising technique specifically tailored to mitigate Gaussian noise in bionic-related imagery. The methodology employs the Discrete Wavelet Transform (DWT), a powerful multi-resolution analysis tool, to decompose the noisy image into various frequency sub-bands. A novel thresholding scheme is then applied to the wavelet coefficients, which effectively separates the noise from the essential image features, preserving critical edges and fine details pertinent to bionic function. Following the thresholding process, the image is reconstructed using the Inverse DWT (IDWT). Performance evaluation is conducted using standard metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) on a dataset of simulated bionic images contaminated with varying levels of Gaussian noise. The results demonstrate that the proposed DWT-based approach out performs conventional spatial-domain filtering and other wavelet-based techniques by achieving a superior balance between noise suppression and the preservation of crucial diagnostic and operational information, thereby improving the reliability and precision of bionic systems.