Multimodal microscopy offers high flexibilities for biomedical observation and diagnosis. Conventional multimodal approaches either use multiple cameras or a single camera spatially multiplexing different modes. The former needs expertise demanding alignment and the latter suffers from limited spatial resolution. Here, we report an alignment-free full-resolution simultaneous fluorescence and quantitative phase imaging approach using single-pixel detectors. By combining reference-free interferometry with single-pixel detection, we encode the phase and fluorescence of the sample in two detection arms at the same time. Then we employ structured illumination and the correlated measurements between the sample and the illuminations for reconstruction. The recovered fluorescence and phase images are inherently aligned thanks to single-pixel detection. To validate the proposed method, we built a proof-of-concept setup for first imaging the phase of etched glass with the depth of a few hundred nanometers and then imaging the fluorescence and phase of the quantum dot drop. This method holds great potential for multispectral fluorescence microscopy with additional single-pixel detectors or a spectrometer. Besides, this cost-efficient multimodal system might find broad applications in biomedical science and neuroscience.
We report a new speckle noise removal algorithm in optical coherence tomography (OCT). Though wavelet domain thresholding algorithms have demonstrated superior advantages in suppressing noise magnitude and preserving image sharpness in OCT, the wavelet tree structure has not been investigated in previous applications. In this work, we propose an adaptive wavelet thresholding algorithm via exploiting the tree structure in wavelet coefficients to remove the speckle noise in OCT images. The threshold for each wavelet band is adaptively selected following a special rule to retain the structure of the image across different wavelet layers. Our results demonstrate that the proposed algorithm outperforms conventional wavelet thresholding, with significant advantages in preserving image features.