The Richardson-Lucy deconvolution (RLD) algorithm is widely used in fluorescence microscopy to enhance image sharpness, yet its high computational complexity limits scalability for large three-dimensional (3D) datasets and impedes real-time volumetric visualization. Here, we introduce an accelerated RLD approach using a Wiener-Butterworth unmatched backprojector, termed WB-ARL, which flattens the spectral product between the forward and backprojectors while effectively suppressing high-frequency noise beyond the diffraction limit. WB-ARL reduces the number of iterations required by more than 10-fold compared with conventional RLD while maintaining high-fidelity reconstruction. CUDA acceleration further increases the speed of both methods by 40-fold while maintaining our method's iterative advantage for up to 400× increase over non-CUDA accelerated matched backprojectors. We further analyze its robustness to noise and optical aberrations and validate its performance through 3D reconstructions of both wide-field mouse kidney tissue and confocal cell phantoms. Our results demonstrate that WB-ARL enables high-resolution, high-fidelity 3D imaging with significantly reduced computational cost, offering a scalable solution for high-throughput fluorescence microscopy.
Du et al. (2026) studied this question.