Morphology-Based Noise Reduction Structural Variation and Thresholding in the Bitonic Filter


It was recently designed to represent the innovative concept of signal bitonicity (one local extremum within a specified range) to distinguish from noise, using data ranking and linear operators, as well as the bitonic filter For Image Processing, a fixed circular mask restricted the spatial scope. A new structurally changing bitonic filter is given, which adapts the mask locally, but does not follow noise patterns. New morphological procedures with efficient implementations, as well as an unique formulation of non-iterative directional Gaussian filtering, are included in this filter. A multi-resolution framework for high noise levels is also possible since data thresholds are combined with morphological procedures. In order to put the structurally changing bitonic filter in its proper context, it is described without assuming any prior understanding of morphological filtering. On a wide variety of photos, these have been put through their paces. However, the block-matching 3D filter does not perform as well as the fixed-mask bitonic filter in all but the most extreme noise conditions, while the results are promising for very high levels of noise. While the block-matching 3D filter is better at preserving signal edges, the structurally changing bitonic tends to have less distinctive residual noise in regions of smooth signal. Due to a more efficient implementation than the fixed-mask bitonic filter, processing time is still competitive.

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