An Efficient Component Based Filter for Random Valued Impulse Noise Removal

In this digital world, due to faulty sensors, storage, and channels images videos are often corrupted by impulse noise, which is a frequently occurring noise type in an image. Impulse noise are classified into fixed valued (salt and pepper noise) and random valued impulse noise. This paper proposes an effective noise reduction method for images corrupted by the random valued impulse noise, handling of which is more difficult than the salt and pepper impulse noise. Our method is based on the concept that the impulse noise produces small components (patches) on an image less than 10x10 size. We convert a gray scale image into a binary image and analyze the components less than 10x10 size on various parameters and remove them using neighboring pixel connectivity. Secondly, removed pixels are filled by the estimated value of median filter calculated with the help of neighboring pixels in that area. Comparison of the proposed algorithm with other existing algorithms shows that the proposed component based filter performs better than all other existing algorithms. The visual and quantitative results show that the performance of the algorithm is very good and it handles more than 70% noise.