This study aimed to develop and evaluate a blind-deconvolution framework using the alternating direction method of multipliers (ADMMs) incorporated with weighted L1-norm regularization for light microscopy (LM) images. A presimulation study was performed using the Siemens star phantom prior to conducting the actual experiments. Subsequently, the proposed algorithm and a total generalized variation-based (TGV-based) method were applied to cross-sectional images of a mouse molar captured at 40× and 400× on-microscope magnifications and the results compared, and the resulting images were compared. Both simulation and experimental results confirmed that the proposed deblurring algorithm effectively restored the LM images, as evidenced by the quantitative evaluation metrics. In conclusion, this study demonstrated that the proposed deblurring algorithm can efficiently improve the quality of LM images.

Three-dimensional (3D) datasets were recorded of gold nanoparticles placed on both sides of silicon nitride membranes using focal series aberration-corrected scanning transmission electron microscopy (STEM). Deconvolution of the 3D datasets was applied to obtain the highest possible axial resolution. The deconvolution involved two different point spread functions, each calculated iteratively via blind deconvolution. Supporting membranes of different thicknesses were tested to study the effect of beam broadening on the deconvolution. It was found that several iterations of deconvolution was efficient in reducing the imaging noise. With an increasing number of iterations, the axial resolution was increased, and most of the structural information was preserved. Additional iterations improved the axial resolution by maximal a factor of 4 to 6, depending on the particular dataset, and up to 8 nm maximal, but also led to a reduction of the lateral size of the nanoparticles in the image. Thus, the deconvolution procedure optimized for the highest axial resolution is best suited for applications where one is interested in the 3D locations of nanoparticles only.

A technique for reconstructing diffraction-limited image of an object from speckle images without reference star is applied to both simulated and real data. The object spectrum is estimated by blind deconvolution using the power spectrum of the speckle images and the phase is restored from the bispectrum.