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

Abstract

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.