Image Restoration Algorithms

J. A. Conchello and E. W. Hansen, Enhanced 3-D reconstruction from confocal scarming microscope images. I. Deterministic and maximum likelihood reconstructions, Appl. Opt. v., 1990, 29, 3795-3804 see also J. Markham and J.-A. Conchello, Fast maximum- likelihood image-restoration algorithms for three dimensional fluorescence microscopy. J. Opt. Soc. Am. A, V. 18, (2001) p. 1062. 

Image restoration algorithm Point spread function... 

The obvious image restoration algorithm is the so-called inverse filter. Given that... 

Fig. 2. Deep K, ixnage of the field of FSC10214+4724 obtained on the W.M. Kedc telescope. On the left the foil 38"x38" field is shown. The FWHM of stellar images in this image is 0.6" and the faintest objects seen in the image have K, 22 mag. On the right the central 10" x 10" around the infrared source is diown in an enlargement where the Ridiardson-Lucy image restoration algorithm has been applied to enhance low levd features.

Lanteri, H., Roche, M., and Aime, C., 2002, Penalized maximum likelihood image restoration with positivity constraints multiplicative algorithms. Inverse Problems 18, 1397... 

The direct demodulation algorithm provides a general approach to handle a large variety of image restoration or reconstruction problems. Computer simulations and analysis results for COS-B and CGRO 7-ray data show that in comparison with traditional techniques, e.g. maximum entropy method, cross-correlation deconvolution or likelihood approach, the direct demodulation method has high sensitivity, high resolution ability and capability to effectively reduce the effect of statistical fluctuations and noise in data and to simultaneously restore both the extended and discrete features in the object. 

Y. Li and F. Santosa. A computational algorithm for minimizing total variation in image restoration. IEEE Trans. Image Process., 5(6) 987-995,1996. 

Color Science Computer Algorithms Computer Architecture Image-Guided Surgery Image Restoration, Maximum Entropy Methods... 

The adaptive estimation of the pseudo-inverse parameters a n) consists of the blocks C and E (Fig. 1) if the transformed noise ( ) has unknown properties. Bloek C performes the restoration of the posterior PDD function w a,n) from the data a (n) + (n). It includes methods and algorithms for the PDD function restoration from empirical data [8] which are based on empirical averaging. Beeause the noise is assumed to be a stationary process with zero mean value and the image parameters are constant, the PDD function w(a,n) converges, at least, to the real distribution. The posterior PDD funetion is used to built a back loop to block B and as a direct input for the estimator E. For the given estimation criteria f(a,d) an optimal estimation a (n) can be found from the expression... 

Using spectral image data provided by Jansson et al (1970), we restored a portion of the Q branch of the v3 band of CH4. Results are shown in Fig. 4. Note the smoothness of the output, no doubt enforced by the prior conviction of a flat object. But, despite this smoothness, the right-hand line has been split into two components. These same data were also restored by Jansson, using his bound-constrained algorithm (see Chapters 4, 6, and 7... 

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