Quadtree Structured Image Approximation for Denoising and Interpolation - 2014 PROJECT TITLE : Quadtree Structured Image Approximation for Denoising and Interpolation - 2014 ABSTRACT: The success of the many image restoration algorithms is often thanks to their ability to sparsely describe the first signal. Shukla proposed a compression algorithm, based mostly on a sparse quadtree decomposition model, that may optimally represent piecewise polynomial images. During this paper, we tend to adapt this model to the image restoration by changing the speed-distortion penalty to a description-length penalty. In addition, one of the major drawbacks of this sort of approximation is that the computational complexity required to find a appropriate subspace for every node of the quadtree. We have a tendency to address this issue by searching for a suitable subspace abundant a lot of efficiently using the mathematics of updating matrix factorisations. Algorithms are developed to tackle denoising and interpolation. Simulation results indicate that we tend to beat state-of-the-art results when the initial signal is in the model (e.g., depth images) and are competitive for natural images when the degradation is high. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Matrix Algebra Computational Complexity Image Restoration Image Denoising Denoising Interpolation Sparse Regularisation Image Models Piecewise Polynomial Approximation Quadtree Translation Invariant Directional Framelet Transform Combined With Gabor Filters for Image Denoising - 2014 Progressive Image Denoising Through Hybrid Graph Laplacian Regularization A Unified Framework - 2014
