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  4. Filamentous Structures for Content-Aware Image Enhancement
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Category: MTech DIP Projects
By MTech Projects
MTech Projects
24.Nov
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Filamentous Structures for Content-Aware Image Enhancement

PROJECT TITLE :

Content-Aware Enhancement of Images With Filamentous Structures

ABSTRACT:

Enhancement methods for photos with filamentous features are described in this research. For the elimination of clutter and noise from the backdrop, our method employs a gradient sparsity constraint coupled with a filamentous structure constraint. confocal microscopy photos of neurons, calcium imaging data, and photographs of road pavement are used to test the approach. We discovered that our method preserves both the structure and intensity of the original object in the improved photos. In neuron microscopy, we found that neurons increased by our method were better correlated with the original structure intensities than neurons improved by well-known methods. Calcium imaging data simulations show that both the number of neurons discovered and their accuracy have been increased. We discovered more regions of calcium activity in the entire field of vision when we applied our approach to real calcium data. Using our improvement method, we were able to detect smaller or milder cracks in road pavement.

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  • Video Tracking Using Learned Hierarchical Features - 2015
  • Discrete Wavelet Transform and Data Expansion Reduction in Homomorphic Encrypted Domain - 2013
  • Automatic Dynamic Texture Segmentation Using Local Descriptors and Optical Flow - 2013
  • Non-Convex Low-Rank Approximation for Matrix Completion
  • Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding - 2018
  • Towards a no-reference image quality assessment Using statistics of perceptual color descriptors - 2016
  • Segmentation of Blood Vessels and Optic Disc in Retinal Images - 2014
  • Spatio-temporal matching for Human pose estimation in video - 2016
  • Deep Networks Reconstruction of 3D Neurons in a Tangled Neuronal Image
  • Filamentous Structures for Content-Aware Image Enhancement
Previous article: Using a Combination of Local and Global Measures to Assess DIBR-Synthesized Image Quality Using a Combination of Local and Global Measures to Assess DIBR-Synthesized Image Quality Next article: Configurable Haze Removal Contrast Dark Channel Priority-Based Contrast Enhancement Model Configurable Haze Removal Contrast Dark Channel Priority-Based Contrast Enhancement Model
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