A Robust Noise Detector for High Density Impulse Noise

Abstract

We propose a robust adaptive algorithm for detecting random-valued impulse noise even for highly corrupted images. A variant of Rank-Ordered Absolute Difference (ROAD) called Hybrid Dynamical ROAD (HDROAD) is proposed to improve the accuracy of noise detection. A dynamic window selector chooses the size and shape of the window depending on the density and orientation of noise such as in edges. A considerable amount of PSNR and MSE improvement can be done if the noise detector is accurate in finding out the noisy pixel correctly and if it is efficient enough to misclassify the noiseless pixel as corrupted. Also the rank or length parameter used in ROAD is varied according to the need of the noise detector and a cumulative rank (CR) is used to further improve the performance. We validate the performance of our noise detector using two widely used parameters namely Nf and Nm. A Detailed analysis of the algorithm and the simulation results show that the proposed algorithm is robust for detecting noises significantly even for noise level as high as 80%.