Deconvolution is a mathematical process used to reverse the effects of convolution on recorded data. In various fields such as signal processing, image processing, and statistics, convolution is often used to combine two functions, typically representing the input signal and a filter or system response. However, when you want to retrieve the original signal from the convoluted data, you apply deconvolution.
Articles by others on the same topic
Deconvolution is the inverse problem of recovering an input from a known response kernel and measured output . The convolution theorem turns it into , but division by a small amplifies noise and a zero of loses information in the corresponding mode. A unique stable inverse requires assumptions on the operator and admissible inputs; a formal Fourier transform quotient alone does not establish either property.