Information retrieval from optical speckles is desired yet challenging. Insufficient sampling, especially in sub-Nyquist domain, of speckles significantly destroys the encoded information and correlations among these speckle grains. To address that, we trained a deep neural network to combat the physical imperfection: the sub-Nyquist sampled speckles (~14 below the Nyquist criterion) are interpolated up to a well-resolved level (322 times more pixels to resolve the same FOV) with smoothed morphology fine-textured, and more importantly, lost information retraced. With the FOV-resolution dilemma favorably overcome, it deepens our understanding of the scattering, enabling big and clear imaging in complex scenarios.
Edge enhancement is fundamental in image processing for recognizing or highlighting image features. Existing methods can only function in free space or transparent media. It remains challenging to achieve edge enhancement in the presence of multiple scattering. In this work, we present an implementation of digital optical phase conjugation to achieve effective edge enhancement through scattering media. The hypothesis is verified through experiments; the performance is promising and can be tuned by adjusting the beam ratio. The method may potentially enrich the interpretation of images obtained from complex environments.
Light, in many ways, is an ideal form of electromagnetic waves to probe and treat biological tissues. But biomedical optical techniques encounter an inevitable trade-off between resolution and penetration depth due to the strong scattering of light in tissue; existing microscopic optical modalities seldom can see beyond the so-called optical diffusion limit (~1 mm for human skin). In this talk, we summarize our endeavors in the past years of using the synergy of light and sound to achieve high-resolution optical imaging, focusing, and neuron activation in thick biological tissue based on the synergy of light and sound and optical wavefront shaping. Limitations, potential applications, and further direction are also discussed. The work has been supported by the National Natural Science Foundation of China (no. 81671726 and no. 81627805), the Hong Kong Research Grant Council (no. 25204416), the Hong Kong Innovation and Technology Commission (no. ITS/022/18), and the Shenzhen Science and Technology Innovation Commission (no. JCYJ20170818104421564).