We introduce an improved 2D adaptive smoothing algorithm for noise removal and feature preservation. Comparing to the original 2D adaptive smoothing algorithm, this new algorithm is also based on the novel idea of utilizing contextual discontinuity and local discontinuity jointly to detect and distinguish edges and noise. The new algorithm improves the main concept – contextual discontinuity by introducing a novel homogeneity region definition with a corresponding method for contextual discontinuity measurement. Comparing to the original algorithm and other smoothing algorithms, the improved algorithm can preserve edges more effectively while removing noise.
The improved 2D algorithm has been implemented and extensive experiments have been carried out to compare the algorithm to the original algorithm and other smoothing strategies to quantitatively demonstrate improvement in performance. Measurements are applied to evaluate the noise removal and edge preservation performance. Simulation results show that this improved algorithm has a superior performance over both the original algorithm and other popular smoothing strategies in noise removal as well as feature preservation.