Jason Cole • 6/12/2016

Regularisation

This article discusses the concept of regularization in the context of inverse problems, specifically focusing on ill-posed matrix equations and image deconvolution. It explains how small errors in measurements can lead to large errors in solutions when the condition number of a matrix is high, and introduces regularization as a technique to stabilize such problems. The content is mathematical and technical, aimed at readers with a background in linear algebra and signal processing, and includes practical examples from computed tomography and image processing.

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#linear algebra #Regularization #Inverse Problems