There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems. In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a special emphasis on heuristic iterative optimization methods employing modern evolutionary and swarm intelligence based techniques. The applications considered are in domains such as communications engineering, estimation and tracking, digital filter design, wireless sensor networks, bioelectric signal classification, image denoising, and image feature tracking. The book presents interesting, state-of-the-art methodologies for solving real-world problems and it is a suitable reference for researchers and engineers in the areas of heuristics and signal processing.13.11 Denoising of the Original image Lena image corrupted by Gaussian white noise Denoised image o The soft ... As a consequence, it results in a significant variation of the estimation due to the sensitivity of the inverse wavelet transform. In addition ... 13.11, bottom panel) using the Matlab function wdencmp with a fixed soft threshold. The Matlab code for this application is as follows: %Soft thresholding has been chosen Xd I wdencmp(a#39;gbla#39;, x, a#39;sym4a#39;, 2, thr, sorh, keepapp); % Graphical.
|Title||:||Advances in Heuristic Signal Processing and Applications|
|Author||:||Amitava Chatterjee, Hadi Nobahari, Patrick Siarry|
|Publisher||:||Springer Science & Business Media - 2013-06-05|