Xiong Zhen, Melzer Jeffrey E, Garan Jacob, McLeod Euan
Opt Express. 2018 Oct 1;26(20):25676-25692. doi: 10.1364/OE.26.025676.
Lens-free holographic microscopy offers sub-micron resolution over an ultra-large field-of-view >20 mm, making it suitable for bio-sensing applications that require the detection of small targets at low concentrations. Various pixel super-resolution techniques have been shown to enhance resolution and boost signal-to-noise ratio (SNR) by combining multiple partially-redundant low-resolution frames. However, it has been unclear which technique performs best for small-target sensing. Here, we quantitatively compare SNR and resolution in experiments using no regularization, cardinal-neighbor regularization, and a novel implementation of sparsity-promoting regularization that uses analytically-calculated gradients from Bayer-pattern image sensors. We find that sparsity-promoting regularization enhances the SNR by ~8 dB compared to the other methods when imaging micron-scale beads with surface coverages up to ~4%.
无透镜全息显微镜在超过20毫米的超大视场上提供亚微米分辨率，使其适用于需要检测低浓度小目标的生物传感应用。各种像素超分辨率技术已被证明可以通过组合多个部分冗余的低分辨率帧来提高分辨率并提升信噪比（SNR）。然而，目前尚不清楚哪种技术在小目标传感方面表现最佳。在这里，我们在实验中定量比较了无正则化、基数邻域正则化以及一种使用来自拜耳模式图像传感器的解析计算梯度的新型稀疏促进正则化实现方式下的信噪比和分辨率。我们发现，在对表面覆盖率高达约4%的微米级珠子进行成像时，与其他方法相比，稀疏促进正则化可将信噪比提高约8分贝。
