基于层间和层内反卷积的宽视场显微图像去模糊算法

Yanzhi Ding, I. Park, X. Cui, Van Huan Nguyen, Hakil Kim, T. Do, Wei Li
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引用次数: 2

摘要

提出了一种基于层间和层内反卷积的显微图像去模糊算法。利用金字塔结构,利用层间反褶积对粗到细的潜在图像进行估计。层次间算法基于全变分正则化Richardson-Lucy方案,能够在抑制伪影的情况下估计潜在图像。在层间反褶积之后,再进行层内反褶积。在图像的每个金字塔层中,残差反卷积作为层内反卷积方案,进一步恢复图像的边缘和细节。实验表明,ILILD算法可以在较短的时间内估计出潜在图像,结果具有较好的峰值信噪比、较高的图像熵和较少的伪影。
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Inter-level and intra-level deconvolution based image deblurring algorithm for wide field microscopy
This paper proposes an inter-level and intra-level deconvolution based image deblurring algorithm (ILILD) for microscopic images. Pyramid structure is used, and inter-level deconvolution is applied to estimate latent image from coarse level to fine level. The inter-level algorithm is based on total variation regularized Richardson-Lucy scheme, which can estimate latent image with artifacts suppressed. After inter-level deconvolution, intra-level deconvolution is applied. In each pyramid level of image, the residual deconvolution is done as the intra-level deconvolution scheme to recover image edges and details furtherly. Experiments show that ILILD algorithm can estimate latent images in less time and the results have better peak signal to noise ratio, higher image entropies and few artifacts.
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