Adaptive filters for digital image noise smoothing: An evaluation

Six adaptive noise filtering algorithms were implemented and evaluated. There are (1) median filtering, (2) K-nearest neighbor averaging, (3) gradient inverse weighted smoothing, (4) sigma filtering, (5) Lee additive and multiplicative filtering, and (6) modified Wallis filtering. For the sake of comparison, the mean filter was also included. All algorithms were tested on noise corrupted copies of a composite image consisting of a uniform field, a bar pattern of periods increasing from 2 to 20 pixels, printed text, and a military tank sitting on desert terrain. In one test, uniformly distributed noise between gray levels of −32 and 32 was added to the composite image and filtered. In a second test, multiplicative Gaussian noise with mean 1.0 and standard deviation 0.25 was introduced, then filtered. A 7×7 pixel processing window was used in all six adaptive algorithms and the mean filter for both tests. An adaptive filter was used iteratively with varying window sizes to demonstrate the success of iterative adaptive smoothing. Filtering results were evaluated from statistics, examination of transects plotted from each filtered bar pattern, and from visual ranking by a group of observers.