Digital Image Reconstruction: Deblurring and Denoising

Los Alamos National Laboratory, Лос-Аламос, California, United States
Annual Review of Astronomy and Astrophysics (Impact Factor: 33.35). 08/2005; 43(1):139-194. DOI: 10.1146/annurev.astro.43.112904.104850


▪ Abstract Digital image reconstruction is a robust means by which the underlying images hidden in blurry and noisy data can be revealed. The main challenge is sensitivity to measurement noise in the input data, which can be magnified strongly, resulting in large artifacts in the reconstructed image. The cure is to restrict the permitted images. This review summarizes image reconstruction methods in current use. Progressively more sophisticated image restrictions have been developed, including (a) filtering the input data, (b) regularization by global penalty functions, and (c) spatially adaptive methods that impose a variable degree of restriction across the image. The most reliable reconstruction is the most conservative one, which seeks the simplest underlying image consistent with the input data. Simplicity is context-dependent, but for most imaging applications, the simplest reconstructed image is the smoothest one. Imposing the maximum, spatially adaptive smoothing permitted by the data results in t...

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Available from: Amos Yahil, Jan 13, 2014
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    • "Methods of the second category, that are essentially some variants of the least squares, only use the deterministic spatial or spectral structures of the image such as smoothness for regularization. For a comprehensive survey of classic deconvolution methods for image restoration and reconstruction we refer the interested readers to [1] and [2]. "
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    • "By analogy to one-dimensional reference deconvolution methods [42] and to image restoration in optical microscopy [43], we have estimated the PSF using the three-dimensional line shape of a well-resolved and isolated resonance (T92, Figure 1A) and used this in deconvolution algorithms to restore the original, unbroadened spectrum. A variety of deconvolution methods have been described, such as the Wiener filter [44] or maximum entropy methods [45]; in this instance we have found the iterative Richardson-Lucy algorithm [37] to be particularly effective. Regions and HN projections of the in-cell HNCO spectrum of a sample of αSyn expressed within cells before and after deconvolution are shown in Figure 1. "
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    • "where N represents the number of data points, as discussed in [19], and τ is a fixed positive number. "
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