Abstract
Image noise is a common phenomenon that affects the quality of digital images, arising from factors such as sensor imperfections, transmission errors, and environmental conditions. Effective noise removal is essential in numerous applications of digital image processing, including medical imaging, remote sensing, and computer vision, where accurate image representation is critical. This survey paper explores the various types of noise typically encountered in digital images—such as Gaussian, Salt & Pepper, Speckle, and Poisson noise—and evaluates the effectiveness of different filtering techniques used to mitigate their effects. Filters such as Mean, Median, Gaussian, and Wiener filters are examined for their performance in restoring image quality, with comparative analysis based on metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and classification accuracy. Through this survey, we provide a comprehensive understanding of the strengths and weaknesses of these