Simultaneous degradation estimation and restoration of confocal images and performance evaluation by colocalization analysis

• Published in: Journal of microscopy (Oxford) Vol. 218; no. 1; pp. 22 - 36
• Main Authors: ROOMS, F., PHILIPS, W., LIDKE, D. S.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Science Ltd 01.04.2005
• Subjects: Algorithms; Bacterial Proteins - metabolism; Cell Line; Colocalization analysis; confocal microscopy; deconvolution; Green Fluorescent Proteins - metabolism; Humans; Image Processing, Computer-Assisted; Luminescent Proteins - metabolism; Microscopy, Confocal - methods; Microscopy, Confocal - statistics & numerical data; Optics and Photonics; Poisson Distribution; PSF estimation; Receptor, Epidermal Growth Factor - metabolism; Receptor, ErbB-3 - metabolism; Recombinant Fusion Proteins - metabolism; regularization; steerable pyramids
• ISSN: 0022-2720; 1365-2818
• DOI: 10.1111/j.1365-2818.2005.01455.x

Summary A novel method for joint restoration and estimation of the degradation of confocal microscope images is presented. The observed images are degraded due to two sources: blurring due to the band‐limited nature of the optical system [modelled by the point spread function (PSF)], and Poisson noise contaminates the observations due to the discrete nature of the photon detection process. The proposed method iterates noise reduction, blur estimation and deblurring, and applies these steps in two phases, i.e. a training phase and a restoration phase. In the first phase, these three steps are iterated until the blur estimation converges. Noise reduction and blur estimation are performed using steerable pyramids, and the deblurring is performed by the Richardson–Lucy algorithm. The second phase is the actual restoration. From then on, the blur estimation is used as a criterion to measure the image quality. The iterations are stopped when this measure converges, a result that is guaranteed. The integrated method is completely automatic, and no prior information on the image is required. The method has been given the name SPERRIL (Steerable Pyramid‐based Estimation and Regularized Richardson–Lucy restoration). Compared with existing techniques by both objective measures and visual observation, in the SPERRIL‐restored images noise is better suppressed.