Daan Zhu | PubMed Health

PubMed2006

MLEM deconvolution of protein X-ray diffraction images based on a multiple-PSF model.

In this paper we analyze the degradation of protein X-ray diffraction images by diffuse light distortion (DLD). In order to correct the degradation, a new multiple point spread function (PSF) model is introduced and used to restore X-ray diffraction image data (XRD). Raw PSFs are collected from isolated spots in high-resolution areas on the diffraction patterns which represent the orientation of DLDs. An adaptive ridge regression (ARR) technique is used to remove noise from the raw PSF data. A target Gaussian function is used to model the raw PSFs. A maximum likelihood expectation maximization (MLEM) algorithm combined with a multi-PSF model is employed to restore high intensity, asymmetrical protein X-ray diffraction data. Experimental results using a single and multiple PSFs are presented and discussed. We show that using a multiple PSF model in the deconvolution algorithm improved the quality of the XRD and as a result the spot integration error (chi-squared) and corresponding electron density mapare improved.

Algorithms↗

PubMed2005

Adaptive penalty likelihood for reconstruction of multidimensional confocal microscopy images.

In this paper we devise a penalty likelihood with noise constraints method to restore 2D and 3D confocal microscope images. Regularization is a commonly used technique in image restoration to balance restored image quality and noise suppression, but despite this noise is usually amplified. Taking into account common confocal imaging system degradation, we develop an algorithm by using a gradient descent method (PLGDA) to approach the minimum solution of the penalty likelihood equation. A Lagrange parameter controls the balance between the penalty and likelihood terms and is estimated using an adaptive method. We show that the a priori information is key to the regularization and Lagrange parameter estimation. The convergence characteristics are analysed and discussed. PLGDA and a traditional maximum likelihood expectation maximization are used to restore 2D and 3D confocal images. The point spread function (PSF), used to restore the data is collected from an experiment and modelled by bi-cubic splines to give an accurate noise free representation. Our experimental results show that the restored images are significantly improved by PLGDA.

Algorithms↗