A Comprehensive Review on Image Restoration Methods due to Salt and Pepper Noise
G. Prasanna Kumar, J. Vijay Arputharaj, P Ravi Kumar, Debolina Kumar, B. V. V. Satyanarayana, Prudhvi Raj Budumuru · 2023
Digital images are well-use in various fields like satellite communication, mobile communication, medical and security. Visualized information helps the people to understand the things easily by seen. Improper capturing, age of camera lens, imperfect storage and transmission leads to introduce noise in the image. Gaussian noise, salt and pepper/impulse noise and speckle noise may affect the original image due to aforementioned reasons. Out of these, impulse noise/salt and pepper noise is one of the major types, degrades the image with black and white spots it results loss of required information. Hence, restoration of ground- truth image from such type of noisy image is a challenging task to provide quality and clarity visuals to users. Several linear and non-linear methods have been proposed by researchers since more than four decades. Non-linear methods based on; median filtering approach; adaptive median filter approach; median filter with switching condition; and median filter with rank order type; are proposed from early 1980’s onwards. All of these operated directly on pixels in spatial domain. Hence, they are very easy to implement and most of them are not that much robust at middle and higher noise density circumstances. Further, various researchers have been implemented linear methods such as wavelet transform methods like S WT and DWT. Majority of these are works well upto 50% noise density conditions and very few works well on higher and multiple noise density conditions also. To overcome these problems CNN’s based methods have been developed tremendously by various researchers from last decade and these methods require huge database to train the network model. Most of these, achieved good accuracy rates at higher and multiple noise conditions. Hence, here a detailed review report is presented on impulse noise removal methods with their Peak Signal to Noise Ratio (PSNR).