A systematic review of state-of-the-art noise removal techniques in digital images

Digital Image processing is a subcategory of digital signal processing that lays emphasis on the study of processing techniques used for enhancement or restoration. De-noising of images corrupted with various types of noises falls into this category. De-noising is mainly performed to enhance the understandability of an affected image. Images captured with faulty equipment or being transmitted over long distances are highly prone to be depraved by impulse noise, so, various techniques are presented for removal of this noise from images. Each of the presented technique has its own merits and demerits. This paper presents a comprehensive comparative analysis of these techniques over a wide range of noise densities. All the filtering techniques are implemented in MATLAB and simulated with standard benchmark image data and qualitative metrics namely Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are evaluated and compared. Therefore, this paper presents a comprehensive comparative analysis of various state-of-the-art noise removal techniques.