Analysis of methods and algorithms for image filtering and quality enhancement

Abstract

This paper addresses the prevalent issue of image noise and presents methods for its mitigation. The paper describes, analyses
and tests a variety of image filtering techniques, with specific reference to their use in different contexts. The filtering methods can
be classified into two principal categories: linear filters, which include the Gaussian and mean filters, and non-linear filters, which
comprise the median filter, the Fast Fourier Transform (FFT), the Non-Local Means (NLM) filter, and the anisotropic diffusion filter.
The efficacy of each filter is mathematically described and evaluated on RGB images using the Python programming language. The
study delineates the evaluation metrics and their respective advantages and disadvantages. The Root Mean Square Error (RMSE) and
Peak Signal-to-Noise Ratio (PSNR) are employed as criteria for the analysis of algorithm efficiency. Furthermore, the mean execution time for each algorithm is also monitored. The experimental data suggests that linear filters are relatively fast but produce inferior results and are best employed as preparatory measures. Non-linear filters have been demonstrated to be more robust and applicable
to a variety of noise types, although it has been established that they require parameter fine-tuning. The study demonstrates that anisotropic diffusion is suitable for both manual image processing and real-time applications, offering an optimal balance between processing speed and denoised image quality. NLM is optimal for high-quality single image processing due to its superior results, despite a slower processing speed. FFT is noted for its efficiency in eliminating periodic noise. Further research will be conducted on
advancing filtering techniques for different real-world scenarios and autonomous systems.