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1. Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images

   Rao, Bovey Y; Peterson, Alexis M; Kandror, Elena K; Herrlinger, Stephanie; Losonczy, Attila; Paninski, Liam; Rizvi, Abbas H; Varol, Erdem (September 2021, Medical Image Computing and Computer Assisted Intervention – MICCAI 2021)

   de Bruijne, M. (Ed.)

   Spatial transcriptomics techniques such as STARmap [15] enable the subcellular detection of RNA transcripts within complex tissue sections. The data from these techniques are impacted by optical microscopy limitations, such as shading or vignetting effects from uneven illumination during image capture. Downstream analysis of these sparse spatially resolved transcripts is dependent upon the correction of these artefacts. This paper introduces a novel non-parametric vignetting correction tool for spatial transcriptomic images, which estimates the illumination field and background using an efficient iterative sliced histogram normalization routine. We show that our method outperforms the state-of-the-art shading correction techniques both in terms of illumination and background field estimation and requires fewer input images to perform the estimation adequately. We further demonstrate an important downstream application of our technique, showing that spatial transcriptomic volumes corrected by our method yield a higher and more uniform gene expression spot-calling in the rodent hippocampus. Python code and a demo file to reproduce our results are provided in the supplementary material and at this github page: https://github.com/BoveyRao/Non-parametric-vc-for-sparse-st. 

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2. Fluorescent wavefront shaping using incoherent iterative phase conjugation

   https://doi.org/10.1364/FIO.2022.JTu5B.4  

   Aizik, Dror; Gkioulekas, Ioannis; Levin, Anat (January 2022, Frontiers in Optics + Laser Science 2022 (FIO, LS))

   We extend iterative phase conjugation algorithms, previously derived for coherent illumination. We show they can be used to focus on incoherent fluorescent sources, and the incoherent emission largely expands penetration depth and convergence speed. 

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3. In vivo lensless microscopy via a phase mask generating diffraction patterns with high-contrast contours

   https://doi.org/10.1038/s41551-022-00851-z  

   Adams, Jesse K.; Yan, Dong; Wu, Jimin; Boominathan, Vivek; Gao, Sibo; Rodriguez, Alex V.; Kim, Soonyoung; Carns, Jennifer; Richards-Kortum, Rebecca; Kemere, Caleb; et al (May 2022, Nature Biomedical Engineering)

   Abstract The simple and compact optics of lensless microscopes and the associated computational algorithms allow for large fields of view and the refocusing of the captured images. However, existing lensless techniques cannot accurately reconstruct the typical low-contrast images of optically dense biological tissue. Here we show that lensless imaging of tissue in vivo can be achieved via an optical phase mask designed to create a point spread function consisting of high-contrast contours with a broad spectrum of spatial frequencies. We built a prototype lensless microscope incorporating the ‘contour’ phase mask and used it to image calcium dynamics in the cortex of live mice (over a field of view of about 16 mm 2 ) and in freely moving Hydra vulgaris , as well as microvasculature in the oral mucosa of volunteers. The low cost, small form factor and computational refocusing capability of in vivo lensless microscopy may open it up to clinical uses, especially for imaging difficult-to-reach areas of the body. 

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4. FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

   https://doi.org/10.1109/TPAMI.2020.3033882  

   Khan, Salman Siddique; Sundar, Varun; Boominathan, Vivek; Veeraraghavan, Ashok; Mitra, Kaushik (October 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence)

   null (Ed.)

   Lensless imaging has emerged as a potential solution towards realizing ultra-miniature cameras by eschewing the bulky lens in a traditional camera. Without a focusing lens, the lensless cameras rely on computational algorithms to recover the scenes from multiplexed measurements. However, the current iterative-optimization-based reconstruction algorithms produce noisier and perceptually poorer images. In this work, we propose a non-iterative deep learning-based reconstruction approach that results in orders of magnitude improvement in image quality for lensless reconstructions. Our approach, called FlatNet, lays down a framework for reconstructing high-quality photorealistic images from mask-based lensless cameras, where the camera's forward model formulation is known. FlatNet consists of two stages: (1) an inversion stage that maps the measurement into a space of intermediate reconstruction by learning parameters within the forward model formulation, and (2) a perceptual enhancement stage that improves the perceptual quality of this intermediate reconstruction. These stages are trained together in an end-to-end manner. We show high-quality reconstructions by performing extensive experiments on real and challenging scenes using two different types of lensless prototypes: one which uses a separable forward model and another, which uses a more general non-separable cropped-convolution model. Our end-to-end approach is fast, produces photorealistic reconstructions, and is easy to adopt for other mask-based lensless cameras. 

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5. Learning to Sense for Coded Diffraction Imaging

   https://doi.org/10.3390/s22249964  

   Hyder, Rakib; Cai, Zikui; Asif, M. Salman (December 2022, Sensors)

   In this paper, we present a framework to learn illumination patterns to improve the quality of signal recovery for coded diffraction imaging. We use an alternating minimization-based phase retrieval method with a fixed number of iterations as the iterative method. We represent the iterative phase retrieval method as an unrolled network with a fixed number of layers where each layer of the network corresponds to a single step of iteration, and we minimize the recovery error by optimizing over the illumination patterns. Since the number of iterations/layers is fixed, the recovery has a fixed computational cost. Extensive experimental results on a variety of datasets demonstrate that our proposed method significantly improves the quality of image reconstruction at a fixed computational cost with illumination patterns learned only using a small number of training images. 

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