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.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Lane Detection of Curving Road for Structural High-way with Straight-curve Model on Vision ABSTRACT: Curve is a traffic accident-prone place in the structural road's traffic system. A problematic aspect for assisted
PROJECT TITLE : Salient Object Detection With Lossless Feature Reflection and Weighted Structural Loss ABSTRACT: As more and more real-world applications emerge, salient object identification, which tries to identify and find
PROJECT TITLE :Reweighted Low-Rank Matrix Analysis With Structural Smoothness for Image Denoising - 2018ABSTRACT:In this Project, we tend to develop a brand new low-rank matrix recovery algorithm for image denoising. We have
PROJECT TITLE :A Probabilistic Framework for Structural Analysis and Community Detection in Directed Networks - 2018ABSTRACT:There's growing interest in structural analysis of directed networks. Two major points that require
PROJECT TITLE :Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach - 2017ABSTRACT:We tend to propose a easy however effective structural patch decomposition approach for multi-exposure image fusion (MEF)

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry