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

Abstract

SummaryA 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 Pyramidbased 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.

Estimating missing information by maximum likelihood deconvolution

“…based on directional pyramid filters (Rooms et al, 2005) or based on total variation (Chan and Wong, 1998;Vogel and Oman, 1998) with their constraints better suited for typical objects such as circular discs or regions of equal intensity in the cell nucleus are expected to yield superior performance here.…”

“…The introduction of other constraints can further enhance the image quality as for example the method of sieve kernels (Markham and Conchello, 1997), Good's roughness penalty (Verveer et al, 1999) or wavelet-based approaches (Rooms and Philips, 2002;Rooms et al, 2005).…”

Total variation versus wavelet‐based methods for image denoising in fluorescence lifetime imaging microscopy

“…In addition, precision and accuracy evaluation of multi-exponential decay lifetime determination is also an important issue and will be investigated in combination with FLIM image denoising. Finally, since precise and accurate noise removal can enhance other image processing techniques including deconvolution (with 3D image slicing) / deblurring [53], segmentation, and object tracking, the combination of denoising and these techniques could be employed for FLIM use, as well.…”

Wavelet-based restoration methods: application to 3D confocal microscopy images

“…When Poisson noise is considered, pre-processing stage to stabilize the noise variance is applied previously on the data. Several drawbacks can be pointed out : we wonder whether the PSF is modified during the denoising step (in 16 they estimate it after denoising); but also, we are not sure of the noise nature after the denoising step. Wavelets have shown to be really efficient in denoising problems, which leads naturally to method 1.…”

“…We discuss in this part the advantages and drawbacks of the different approaches. The denoising step often corresponds to a wavelet coefficient thresholding using wavelets 4, 5 or steerable pyramids 16 . Concerning the deconvolution step, RL algorithm or a MAP (Maximum A Posteriori) method is often used.…”