Digital image processing plays an important role in areas such as medical imaging, remote sensing, computer vision, and industrial automation. However, digital images are often affected by different types of noise during image acquisition and transmission, which reduces image quality and affects accurate analysis. This project focuses on the implementation and comparison of various noise reduction techniques for improving image quality. Different types of noise, including Gaussian, Salt & Pepper, Speckle, and Poisson noise, are introduced into digital images to simulate real-world degradation conditions. Filtering techniques such as Gaussian, Median, Wiener, and Bilateral filters are applied to remove noise while preserving important image details and edges. The performance of each filtering method is evaluated using image quality metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Experimental results show that different filters perform effectively for specific noise types, and selecting an appropriate denoising technique significantly improves image quality and restoration performance.
S et al. (Thu,) studied this question.