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Register a new userNoninvasive super-resolution imaging through scattering media
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Super-resolution imaging with advanced optical systems has been revolutionizing technical analysis in various fields from biological to physical sciences. However, many objects are hidden by strongly scattering media such as rough wall corners or biological tissues that scramble light paths, create speckle patterns and hinder objects visualization, let alone super-resolution imaging. Here, we realize a method to do non-invasive super-resolution imaging through scattering media based on stochastic optical scattering localization imaging (SOSLI) technique. Simply by capturing multiple speckle patterns of photo-switchable emitters in our demonstration, the stochastic approach utilizes the speckle correlation properties of scattering media to retrieve an image with more than five-fold resolution enhancement compared to the diffraction limit, while posing no fundamental limit in achieving higher spatial resolution. More importantly, we demonstrate our SOSLI to do non-invasive super-resolution imaging through not only optical diffusers, i.e. static scattering media, but also biological tissues, i.e. dynamic scattering media with decorrelation of up to 80%. Our approach paves the way to non-invasively visualize various samples behind scattering media at unprecedented levels of detail.
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Extending super-resolution imaging techniques to objects hidden in strongly scattering media potentially revolutionize the technical analysis for much broader categories of samples, such as biological tissues. The main challenge is the medias inhomogeneous structures which scramble the light path and create noise-like speckle patterns, hindering the objects visualization even at a low-resolution level. Here, we propose a computational method relying on the objects spatial and temporal fluctuation to visualize nanoscale objects through scattering media non-invasively. The fluctuating object can be achieved by random speckle illumination, illuminating through dynamic scattering media, or flickering emitters. The optical memory effect allows us to derive the object at diffraction limit resolution and estimate the point spreading function (PSF). Multiple images of the fluctuating object are obtained by deconvolution, then super-resolution images are achieved by computing the high order cumulants. Non-linearity of high order cumulant significantly suppresses the noise and artifacts in the resulting images and enhances the resolution by a factor of $sqrt{N}$, where $N$ is the cumulant order. Our non-invasive super-resolution speckle fluctuation imaging (NISFFI) presents a nanoscopy technique with very simple hardware to visualize samples behind scattering media.
Imaging through scattering media is a useful and yet demanding task since it involves solving for an inverse mapping from speckle images to object images. It becomes even more challenging when the scattering medium undergoes dynamic changes. Various approaches have been proposed in recent years. However, to date, none is able to preserve high image quality without either assuming a finite number of sources for dynamic changes, assuming a thin scattering medium, or requiring the access to both ends of the medium. In this paper, we propose an adaptive inverse mapping (AIP) method which is flexible regarding any dynamic change and only requires output speckle images after initialization. We show that the inverse mapping can be corrected through unsupervised learning if the output speckle images are followed closely. We test the AIP method on two numerical simulations, namely, a dynamic scattering system formulated as an evolving transmission matrix and a telescope with a changing random phase mask at a defocus plane. Then we experimentally apply the AIP method on a dynamic fiber-optic imaging system. Increased robustness in imaging is observed in all three cases. With the excellent performance, we see the great potential of the AIP method in imaging through dynamic scattering media.
This work presents a new super-resolution imaging approach by using subwavelength hole resonances. We employ a subwavelength structure in which an array of tiny holes are etched in a metallic slab with the neighboring distance $ell$ that is smaller than half of the wavelength. By tuning the incident wave at resonant frequencies, the subwavelength structure generates strong illumination patterns that are able to probe both low and high spatial frequency components of the imaging sample sitting above the structure. The image of the sample is obtained by performing stable numerical reconstruction from the far-field measurement of the diffracted wave. It is demonstrated that a resolution of $ell/2$ can be obtained for reconstructed images, thus one can achieve super-resolution by arranging multiple holes within one wavelength. The proposed approach may find applications in wave-based imaging such as electromagnetic and ultrasound imaging. It attains two advantages that are important for practical realization. It avoids the difficulty to control the distance the between the probe and the sample surface with high precision. In addition, the numerical reconstructed images are very stable against noise by only using the low frequency band of the far-field data in the numerical reconstruction.
Raman microscopy is a valuable tool for detecting physical and chemical properties of a sample material. When probing nanomaterials or nanocomposites the spatial resolution of Raman microscopy is not always adequate as it is limited by the optical diffraction limit. Numerical post-processing with super-resolution algorithms provides a means to enhance resolution and can be straightforwardly applied. The aim of this work is to present interior-point least squares (IPLS) as a powerful tool for super-resolution in Raman imaging through constrained optimisation. IPLSs potential for super-resolution is illustrated on numerically generated test images. Its resolving power is demonstrated on Raman spectroscopic data of a polymer nanowire sample. Comparison to AFM data of the same sample substantiates that the presented method is a promising technique for analysing nanomaterial samples.
Fourier ptychographic microscopy is a computational imaging technique that provides quantitative phase information and high resolution over a large field-of-view. Although the technique presents numerous advantages over conventional microscopy, model mismatch due to unknown optical aberrations can significantly limit reconstruction quality. Many attempts to address this issue rely on embedding pupil recovery into the reconstruction algorithm. In this paper we demonstrate the limitations of a purely algorithmic approach and evaluate the merits of implementing a simple, dedicated calibration procedure. In simulations, we find that for a target sample reconstruction error, we can image without any aberration corrections up to a maximum aberration magnitude of $lambda$/40. When we use algorithmic self-calibration, we can increase the aberration magnitude up to $lambda$/10, and with our in situ speckle calibration technique, this working range is extended further to a maximum aberration magnitude of $lambda$/3. Hence, one can trade-off complexity for accuracy by using a separate calibration process, which is particularly useful for larger aberrations.
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