基于稀疏连续反卷积的光学相干断层扫描分辨率提升
Resolution Improvement for OpticalCoherence Tomography based on Sparse Continuous Deconvolution
AI总结：
提出一种基于稀疏连续反卷积的OCT图像分辨率提升方法，利用稀疏性和连续性正则化约束迭代并采用Bregman分裂求解，有效抑制了传统反卷积伪影，在体模和生物样本上实现近两倍分辨率提升。
摘要：
我们提出了一种基于稀疏连续反卷积的光学相干断层扫描（OCT）图像分辨率提升方法。传统反卷积技术（如Lucy-Richardson反卷积）在少量迭代后会出现伪影收敛问题，限制了其实际应用。在本研究中，我们利用关于样本稀疏性和连续性的先验知识来约束反卷积迭代过程。通过分辨率保持正则化项利用稀疏性实现分辨率提升；同时引入基于不同方向灰度值相关性的连续性正则化项，以缓解过度图像稀疏并实现降噪。随后采用Bregman分裂技术求解该优化问题。对体模和生物样本的数值模拟及实验研究表明，该方法能有效抑制传统反卷积技术的伪影，并展现出清晰的分辨率提升。对于可定量评估的体模微珠图像，该方法实现了近两倍的分辨率提升。
英文摘要：
We propose an image resolution improvement method for optical coherence tomography (OCT) based on sparse continuous deconvolution. Traditional deconvolution techniques such as Lucy-Richardson deconvolution suffers from the artifact convergence problem after a small number of iterations, which brings limitation to practical applications. In this work, we take advantage of the prior knowledge about the sample sparsity and continuity to constrain the deconvolution iteration. Sparsity is used to achieve the resolution improvement through the resolution preserving regularization term. And the continuity based on the correlation of the grayscale values in different directions is introduced to mitigate excessive image sparsity and noise reduction through the continuity regularization term. The Bregman splitting technique is then used to solve the resulting optimization problem. Both the numerical simulation study and experimental study on phantoms and biological samples show that our method can suppress artefacts of traditional deconvolution techniques effectively. Meanwhile, clear resolution improvement is demonstrated. It achieved nearly twofold resolution improvement for phantom beads image that can be quantitatively evaluated
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