My Account | Careers | Downloads | Blog

PROJECT TITLE :

Anisotropic Guided Filtering

ABSTRACT:

They've been widely used in numerous Image Processing and computer vision applications because of their low complexity as well as good edge-preservation capabilities. "Detail halos" appear when the different forms of the guided filter are unable to deal with increasingly aggressive filtering strengths, despite this success. These current filters, on the other hand, function badly when the input and guide images are structurally inconsistent. For example, the guided filter operates as a variable-strength locally-isotropic filter that, in fact, serves as a weak anisotropic filter on the picture, as demonstrated in this paper. When guided filter versions such as the adaptive guided filter (AGF), weighted guided image filter (WGIF), and gradient-domain-guided-image filter (GD-GIF) are used, they tend to exhibit this behaviour (GGIF). AnisGF (Anisotropic Guided Filter) is a new filter that uses weighted averaging to maximise diffusion while maintaining sharp image edges. In order to provide strong anisotropic filtering while keeping low computational costs, the proposed weights have been optimised based on local neighbourhood variances. The results of synthetic experiments reveal that the suggested method tackles the issue of detail haloes and the management of inconsistent structures reported in prior iterations of the guided filter.. Scale-aware filtering, texture removal and upsampling of chroma all highlight the benefits of the technique in practise.


Did you like this research project?

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

PROJECT TITLE : Multi-Access Filtering for Privacy-Preserving Fog Computing ABSTRACT: The military and other traditionally conservative and sensitive sectors, such as government and law enforcement, are beginning to show an increased
PROJECT TITLE : Weighted Guided Image Filtering With Steering Kernel ABSTRACT: The guided image filter (GIF) is prone to halo artefacts at the margins because of its local characteristic. As a workaround, a weighted guided image
PROJECT TITLE : Fast Adaptive Bilateral Filtering ABSTRACT: For edge-preserving smoothing, a fixed Gaussian range kernel and a spatial kernel are employed in the bilateral filter. It is possible to generalise this filter by allowing
PROJECT TITLE : Fast High-Dimensional Bilateral and Nonlocal Means Filtering ABSTRACT: Currently available rapid methods for bilateral and nonlocal means filtering are limited to grayscale images. High-dimensional data, such as
PROJECT TITLE :PHD and CPHD Filtering With Unknown Detection Probability - 2018ABSTRACT:A priori knowledge of target detection likelihood is of vital importance within the Gaussian mixture probability hypothesis density (PHD)

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

Project Enquiry