Publication

Abstract : Image denoising is crucial for enhancing image quality, especially in medical applications where noise can significantly impact the accuracy of analysis and interpretation. This paper presents the development of an adaptive Gaussian filter-based denoising technique that effectively enhances images corrupted by various types of noise. By incorporating the adaptive adjustment of filter parameters based on local image characteristics, the proposed method achieves superior denoising performance. The algorithm analyzes the noisy image to estimate the noise characteristics, dynamically adjusting the Gaussian filter parameters to ensure optimal preservation of image details while effectively suppressing noise artifacts. Optimized strategies for parameter selection and filtering operations are employed to ensure computational efficiency. A comparative analysis demonstrates that the adaptive Gaussian filter outperforms traditional methods, achieving a higher Peak Signal-to-Noise Ratio (PSNR) and a lower Root Mean Square Error (RMSE). The technique also exhibits robustness against different noise distributions, making it a versatile solution for various image enhancement applications. These findings highlight the potential of the adaptive Gaussian filter to significantly improve image quality, facilitating more accurate and reliable analysis across diverse domains.