AI Article Synopsis
- The authors introduce a technique for removing background noise in light field microscopy (LFM) 3D reconstructions using sparsity and Hessian regularization.
- They enhance the 3D Richardson-Lucy (RL) deconvolution by incorporating total variation (TV) regularization, which helps in noise suppression.
- Results show that this new method outperforms existing techniques in reducing background noise and improving detail, making it valuable for high-quality biological imaging.
Article Abstract
In this Letter, we present a method aiming at background noise removal in the 3D reconstruction of light field microscopy (LFM). Sparsity and Hessian regularization are taken as two prior knowledges to process the original light field image before 3D deconvolution. Due to the noise suppression function of total variation (TV) regularization, we add the TV regularization term to the 3D Richardson-Lucy (RL) deconvolution. By comparing the light field reconstruction results of our method with another state-of-the-art method that is also based on RL deconvolution, the proposed method shows improved performance in terms of removing background noise and detail enhancement. This method will be beneficial to the application of LFM in biological high-quality imaging.
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| http://dx.doi.org/10.1364/OL.482445 | DOI Listing |
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