Abstract:
Using data ranking and linear operators, the bitonic filter distinguishes signal bitonicity (one local extremum within a set range) from noise. A circular mask locally constrained image processing. A novel structurally varying bitonic filter localizes the mask without following noise patterns in natural images.
This new filter uses efficient robust structurally varying morphological operations and a novel formulation of non-iterative directional Gaussian filtering.
Data thresholds are integrated with morphological operations to reduce noise for low noise and enable multi-resolution for high noise. To contextualize the structurally varying bitonic filter, it is compared to high-performance linear noise-reduction filters.
These are tested on many images at various noise levels. The new filter outperforms anisotropic diffusion, image-guided filtering, non-local means at all noise levels, but not the block-matching 3D filter, though results are promising for very high noise.
The structurally varying bitonic preserves signal edges and has less characteristic residual noise in smooth signal regions than the block-matching 3D filter, but it loses small scale detail. Though slower than the fixed-mask bitonic filter, the efficient implementation keeps processing time competitive.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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