Low-dose imaging denoising with one pair of noisy images.

Low-dose imaging techniques have many important applications in diverse fields, from biological engineering to materials science. Samples can be protected from phototoxicity or radiation-induced damage using low-dose illumination. However, imaging under a low-dose condition is dominated by Poisson noise and additive Gaussian noise, which seriously affects the imaging quality, such as signal-to-noise ratio, contrast, and resolution. In this work, we demonstrate a low-dose imaging denoising method that incorporates the noise statistical model into a deep neural network. One pair of noisy images is used instead of clear target labels and the parameters of the network are optimized by the noise statistical model. The proposed method is evaluated using simulation data of the optical microscope, and scanning transmission electron microscope under different low-dose illumination conditions. In order to capture two noisy measurements of the same information in a dynamic process, we built an optical microscope that is capable of capturing a pair of images with independent and identically distributed noises in one shot. A biological dynamic process under low-dose condition imaging is performed and reconstructed with the proposed method. We experimentally demonstrate that the proposed method is effective on an optical microscope, fluorescence microscope, and scanning transmission electron microscope, and show that the reconstructed images are improved in terms of signal-to-noise ratio and spatial resolution. We believe that the proposed method could be applied to a wide range of low-dose imaging systems from biological to material science.

Three-dimensional phase and intensity reconstruction from coherent modulation imaging measurements.

Coherent modulation imaging is a lensless imaging technique, where a complex-valued image can be recovered from a single diffraction pattern using the iterative algorithm. Although mostly applied in two dimensions, it can be tomographically combined to produce three-dimensional (3D) images. Here we present a 3D reconstruction procedure for the sample's phase and intensity from coherent modulation imaging measurements. Pre-processing methods to remove illumination probe, inherent ambiguities in phase reconstruction results, and intensity fluctuation are given. With the projections extracted by our method, standard tomographic reconstruction frameworks can be used to recover accurate quantitative 3D phase and intensity images. Numerical simulations and optical experiments validate our method.

Coherent modulation imaging using a physics-driven neural network.

Coherent modulation imaging (CMI) is a lessness diffraction imaging technique, which uses an iterative algorithm to reconstruct a complex field from a single intensity diffraction pattern. Deep learning as a powerful optimization method can be used to solve highly ill-conditioned problems, including complex field phase retrieval. In this study, a physics-driven neural network for CMI is developed, termed CMINet, to reconstruct the complex-valued object from a single diffraction pattern. The developed approach optimizes the network's weights by a customized physical-model-based loss function, instead of using any ground truth of the reconstructed object for training beforehand. Simulation experiment results show that the developed CMINet has a high reconstruction quality with less noise and robustness to physical parameters. Besides, a trained CMINet can be used to reconstruct a dynamic process with a fast speed instead of iterations frame-by-frame. The biological experiment results show that CMINet can reconstruct high-quality amplitude and phase images with more sharp details, which is practical for biological imaging applications.

Resolution-enhanced ptychography framework with an equivalent upsampling and precise position.

As a lensless imaging technique, ptychography provides a new way to resolve the conflict between the spatial resolution and the field of view. However, due to the pixel size limit of the sensor, a compromise has to be reached between the spatial resolution and the signal-to-noise ratio. Here, we propose a resolution-enhanced ptychography framework with equivalent upsampling and subpixel accuracy in position to further improve the resolution of ptychography. According to the theory of pixel superresolved techniques, the inherent shift illumination scheme in ptychography can additionally enhance the resolution with the redundant data. An additional layer of pooling is used to simulate the downsampling of a digital record, and the pixel superresolved problem is transformed into an automatic optimization problem. The proposed framework is verified by optical experiments, both in biological samples and the resolution targets. Compared to the traditional algorithm, the spatial lateral resolution is twice as large using the same data set.

Spatiotemporal coherent modulation imaging for dynamic quantitative phase and amplitude microscopy.

The single-shot capability of coherent modulation imaging (CMI) makes it have great potential in the investigation of dynamic processes. Its main disadvantage is the relatively low signal-to-noise ratio (SNR) which affects the spatial resolution and reconstruction accuracy. Here, we propose the improvement of a general spatiotemporal CMI method for imaging of dynamic processes. By making use of the redundant information in time-series reconstructions, the spatiotemporal CMI can achieve robust and fast reconstruction with higher SNR and spatial resolution. The method is validated by numerical simulations and optical experiments. We combine the CMI module with an optical microscope to achieve quantitative phase and amplitude reconstruction of dynamic biological processes. With the reconstructed complex field, we also demonstrate the 3D digital refocusing ability of the CMI microscope. With further development, we expect the spatiotemporal CMI method can be applied to study a range of dynamic phenomena.

Dynamic coherent diffractive imaging with a physics-driven untrained learning method.

Reconstruction of a complex field from one single diffraction measurement remains a challenging task among the community of coherent diffraction imaging (CDI). Conventional iterative algorithms are time-consuming and struggle to converge to a feasible solution because of the inherent ambiguities. Recently, deep-learning-based methods have shown considerable success in computational imaging, but they require large amounts of training data that in many cases are difficult to obtain. Here, we introduce a physics-driven untrained learning method, termed Deep CDI, which addresses the above problem and can image a dynamic process with high confidence and fast reconstruction. Without any labeled data for pretraining, the Deep CDI can reconstruct a complex-valued object from a single diffraction pattern by combining a conventional artificial neural network with a real-world physical imaging model. To our knowledge, we are the first to demonstrate that the support region constraint, which is widely used in the iteration-algorithm-based method, can be utilized for loss calculation. The loss calculated from support constraint and free propagation constraint are summed up to optimize the network's weights. As a proof of principle, numerical simulations and optical experiments on a static sample are carried out to demonstrate the feasibility of our method. We then continuously collect 3600 diffraction patterns and demonstrate that our method can predict the dynamic process with an average reconstruction speed of 228 frames per second (FPS) using only a fraction of the diffraction data to train the weights.

Polarization-controlled multifrequency coherent perfect absorption in stereometamaterials.

We experimentally demonstrate polarization-controlled multifrequency coherent perfect absorption in stereometamaterials with twisted asymmetrically split rings. The coupling effects in stereometamaterials lead to the mode hybridization and thus multiple electric and magnetic resonances. The coherent perfect absorptions of electric and magnetic modes in stereometamaterials have been verified to be individually switched on/off by an interferometric effect of two counter-propagating coherent beams. The alternation of two orthogonal polarization states enables direct modulation of the operation frequencies of coherent perfect absorptions in both microwave and optical metamaterials. The work provides an opportunity to manipulate coherent perfect absorption and is helpful to design tunable multifrequency absorbers.