#### Wavelet-regularized image deconvolution

A Fast Thresholded Landweber Algorithm for Wavelet-Regularized Multidimensional Deconvolution

Vonesch and Unser (2008)

IEEE Trans. Image Proc. vol. 17(4), pp. 539-549

Quoting the authors, I also like to say that __the recovery of the original image from the observed is an ill-posed problem__. They traced the efforts of wavelet regularization in deconvolution back to a few relatively recent publications by astronomers. Therefore, I guess the topic and algorithm of this paper could drag some attentions from astronomers.

They explain the wavelet based reconstruction procedure in a simple term. The matrix-vector product w_{x}= Wx yields the coefficients of x in the wavelet basis, and W^{T}Wx reconstructs the signal from these coefficients.

Their assumed model is

y=Hx_{orig}+ b,

where y and x_{orig} are vectors containing uniform samples of the original and measured signals; b represents the measurement error. H is a square (block) circulant matrix that approximates the convolution with the PSF. Then, the problem of deconvolution is to find an estimate that maximizes the cost function

J(x) = J_{data}(x)+ λ J_{reg}(x)

They described that “__this functional can also interpreted as a (negative) log-likelihood in a Bayesian statistical framework, and deconvolution can then be seen as a maximum a posteriori (MAP) estimation problem.__” Also the description of the cost function is applicable to the frequently appearing topic in regression or classification problems such as ridge regression, quantile regression, LASSO, LAR, model/variable selection, state space models from time series and spatial statistics, etc.

The observed image is the d-dimensional covolution of an origianl image (the characteristic function of the object of interest) with the

impulse response (or PSF).of the imaging system.

The notion of regularization or penalizing the likelihood seems not well received among astronomers based on my observation that often times the chi-square minimization (the simple least square method) without penalty is suggested and used in astronomical data analysis. Since image analysis with wavelets popular in astronomy, the fast algorithm for wavelet regularized variational deconvolution introduced in this paper could bring faster results to astronomers and could offer better insights of the underlying physical processes by separating noise and background more in a model according fashion, not simple background subtraction.

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