The goal of computer-generated holography (CGH) is to synthesize custom illumination patterns by modulating a coherent light beam. CGH algorithms typically rely on iterative optimization with a built-in trade-off between computation speed and hologram accuracy that limits performance in advanced applications such as optogenetic photostimulation. We introduce a non-iterative algorithm, DeepCGH, that relies on a convolutional neural network with unsupervised learning to compute accurate holograms with fixed computational complexity. Simulations show that our method generates holograms orders of magnitude faster and with up to 41% greater accuracy than alternate CGH techniques. Experiments in a holographic multiphoton microscope show that DeepCGH substantially enhances two-photon absorption and improves performance in photostimulation tasks without requiring additional laser power.

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy’s performance in spike extraction and scalability are state-of-the-art.

Optical coherence tomography (OCT) has become a popular modality in the dermatology discipline due to its moderate resolution and penetration depth. OCT images, however, contain a grainy pattern called speckle. To date, a variety of filtering techniques have been introduced to reduce speckle in OCT images. However, further improvement is required to reduce edge smoothing and the deterioration of small structures in OCT images after despeckling. In this manuscript, we present a novel cluster-based speckle reduction framework (CSRF) that consists of a clustering method, followed by a despeckling method. Since edges are borders of two adjacent clusters, the proposed framework leaves the edges intact. Moreover, the multiplicative speckle noise could be modeled as additive noise in each cluster. To evaluate the performance of CSRF and demonstrate its generic nature, a clustering method, namely k-means (KM), and, two pixelwise despeckling algorithms, including Lee filter (LF) and adaptive Wiener filter (AWF), are used. The results indicate that CSRF significantly improves the performance of despeckling algorithms. These improvements are evaluated on healthy human skin images in vivo using two numerical assessment measures including signal-to-noise ratio (SNR), and structural similarity index (SSIM).

We introduce a computer-generated holography algorithm based on deep learning with unsupervised training. Our method generates high fidelity holograms in a few milliseconds and outperforms alternate methods that require many iterations and longer computation.