Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas

Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas

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Several drawbacks of critically sampled wavelets can be solved by Statistical Analysis and Forecasting of Gender Asymmetry Indexes in the Labor Market of the Orenburg Region overcomplete multiresolution transforms and sparse approximation algorithms.Facing the difficulty to optimize such nonorthogonal and nonlinear transforms, we implement a sparse approximation scheme inspired from the functional architecture of the primary visual cortex.The scheme models simple and complex cell receptive fields through log-Gabor wavelets.

The model also incorporates inhibition and facilitation interactions between neighboring cells.Functionally these interactions allow to extract edges and ridges, providing an edge-based approximation of the visual information.The edge coefficients are shown sufficient for closely reconstructing the images, while contour representations by means Infodemia: rumores, fake news, mitos of chains of edges reduce the information redundancy for approaching image compression.

Additionally, the ability to segregate the edges from the noise is employed for image restoration.