Fast-time consecutive confocal image deblurring using spatiotemporal fused regularization.

Confocal fluorescence microscopy has become a cardinal workhorse instrument in biological research due to its high imaging speed and tissue penetration depth. Unfortunately, the sampled fluorescence signals are intrinsically distorted by optical blurs and photon-counting noise, and the deconvolution method has been introduced to attenuate these degradations. In this paper, we focus mainly on scenarios suffering from severe noise due to low exposure time in a fast-imaging system. To begin with, a Hessian penalty was adopted to depress the artificial staircase effects that were caused by the first-order model (e.g., total variation). Then, to compensate for the weak ability to remove blurring and the produced white-point artifacts of the second-order penalty, we additionally proposed a consistent constraint along the temporal axis based on structural continuity. A remarkable merit of the spatiotemporal fused regularization is retaining the ability of the Hessian matrix to keep details smooth while effectively removing blurring. We employed an alternating-direction-method-of-multipliers algorithm for the corresponding optimization problem. Finally, we conducted experimental comparisons of both the simulated and practical confocal platform, and the excellent performance of the proposed approach reflects the efficiency of the confocal deconvolution work.

Image deconvolution for confocal laser scanning microscopy using constrained total variation with a gradient field.

As for the confocal laser scanning microscope (CLSM) imaging system, the collected weak fluorescence signals are always distorted by optic blur and severe photon-counting noise, and the deconvolution for CLSM images is a typical ill-posed inverse problem, which is highly sensitive to the measurement noise. To promote the reconstruction quality for characteristics of low intensity and strong noise, we employed the prominent total variation regularization (TV) to enforce the sparsity of a fluorescent image gradient with rich details. However, the well-known reconstruction artifacts (e.g., artificial staircase) emerge with TV prior. To settle this issue, we utilized a robust first-order discretization yielding near-isotropy with a gradient field to depress the reconstruction artifacts. Furthermore, the bound constraint was suited to restrain final reconstruction results from appearing unreasonably explosive. For the proposed optimization minimizer with linear constraint, we take one proximal gradient for approximate estimation of each subproblem under the framework of the inexact alternating direction method of multipliers. Moreover, we incorporated a Nesterov's scheme into the numerical method for acceleration of iteration updating. Compared with other competing methods, both the simulation and practical results demonstrate the effectiveness of our proposed model for CLSM image deconvolution.