Unmatched back projector deconvolution for a miniature light field microscope

Wahn, Hannes; Xu, Alec; Poon, Joyce K. S.; Sacher, Wesley D.

doi:10.1117/12.2615671

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gaussian and Butterworth backward kernels construct kernels narrower than forward kernel in spatial domain to make the spectral product flatter. Wiener-Butterworth (WB) backward kernel 10,11 combines the Wiener and Butterworth filters to maximally flatten the spectral product and at the same time suppress spatial frequencies beyond the resolution limit.…”

Section: Introductionmentioning

confidence: 99%

Kernel learning enables fluorescence microscopic image deconvolution with enhanced performance and speed

Cheng,

Lu,

et al. 2024

Preprint

View full text Add to dashboard Cite

The contrast and resolution of fluorescence microscopic images can be effectively improved by classic iterative Richardson-Lucy Deconvolution (RLD) algorithm, but this method is computationally expensive, particularly for three-dimensional data. Variants of RLD with manually designed unmatched backward projector can greatly accelerate deconvolution, however, they require careful parameter optimization to avoid introducing artifacts. Here, we develop Kernel Learning Deconvolution (KLD), which automatically learns forward/backward kernel in RLD from only one paired low-resolution and high-resolution images. The learned kernel reveals a similar pattern with handcrafted Wiener-Butterworth kernel but is more adaptive to data. Besides, it is robust to the signal-to-noise ratio and the number of training samples. KLD shows enhanced deconvolution performance and speed on different cellular structures and imaging modalities, including wide-field microscopy, confocal microscopy, and lattice light-sheet microscopy.

show abstract

Section: Introductionmentioning

confidence: 99%