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1. Illuminant Spectra-Based Source Separation Using Flash Photography

   https://doi.org/10.1109/CVPR.2018.00650  

   Hui, Zhuo; Sunkavalli, Kalyan; Hadap, Sunil; Sankaranarayanan, Aswin C. (June 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition)

   Real-world lighting often consists of multiple illuminants with different spectra. Separating and manipulating these illuminants in post-process is a challenging problem that requires either significant manual input or calibrated scene geometry and lighting. In this work, we leverage a flash/no-flash image pair to analyze and edit scene illuminants based on their spectral differences. We derive a novel physics-based relationship between color variations in the observed flash/no-flash intensities and the spectra and surface shading corresponding to individual scene illuminants. Our technique uses this constraint to automatically separate an image into constituent images lit by each illuminant. This separation can be used to support applications like white balancing, lighting editing, and RGB photometric stereo, where we demonstrate results that outperform state-of-the-art techniques on a wide range of images. 

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2. 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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3. High-efficiency, low-speckle contrast white laser lighting via multi-stage scattering and photon recycling

   https://doi.org/10.21203/rs.3.rs-3182555/v1  

   Liu, Jifeng; Wang, Xiaoxin; Carver, Charles; Shade, Nicholas; Fossum, Eric; Zhou, Xia (August 2023, Research Square)

   Lighting consumes  10% of the global electricity. White laser lighting, utilizing either direct color mixing or phosphor-conversion, can potentially boost the efficiency well beyond existing light emitting diodes (LEDs) especially at high current density. Here we present a compact, universal packaging platform for both laser lighting schemes, which is simultaneously scalable to wavelength division multiplexing for visible light communication. Using commercially available laser diodes and optical components, low-speckle contrast ≤ 5% and uniform illumination is achieved by multi-stage scattering and photon recycling through a mixing rod and static diffusers in a truncated-pyramidal reflective cavity. We demonstrate a high luminous efficacy of 274 lm/W_ofor phosphor-converted laser lighting and 150 lm/W_ofor direct red-green-blue laser mixing, respectively, in reference to the input optical power. In the former case, the luminous efficacy achieved for practical lighting is even higher than most of the previous reports measured using integrating spheres. In the latter case of direct laser color mixing, to our best knowledge, this is the first time to achieve a luminous efficacy approaching their phosphor-conversion counterparts in a compact package applicable to practical lighting. With future improvement of blue laser diode efficiency and development of yellow/amber/orange laser diodes, we envision that this universal white laser package can potentially achieve a luminous efficacy > 275 lm/W_ein reference to the input electrical power, ~ 1.5x higher than state-of-the-art LED lighting and exceeding the target of 249 lm/W_efor 2035. 

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4. Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone

   https://doi.org/10.3390/s19153307  

   Askarian, Behnam; Yoo, Seung-Chul; Chong, Jo Woon (August 2019, Sensors)

   In this paper, we propose a novel strep throat detection method using a smartphone with an add-on gadget. Our smartphone-based strep throat detection method is based on the use of camera and flashlight embedded in a smartphone. The proposed algorithm acquires throat image using a smartphone with a gadget, processes the acquired images using color transformation and color correction algorithms, and finally classifies streptococcal pharyngitis (or strep) throat from healthy throat using machine learning techniques. Our developed gadget was designed to minimize the reflection of light entering the camera sensor. The scope of this paper is confined to binary classification between strep and healthy throats. Specifically, we adopted k-fold validation technique for classification, which finds the best decision boundary from training and validation sets and applies the acquired best decision boundary to the test sets. Experimental results show that our proposed detection method detects strep throats with 93.75% accuracy, 88% specificity, and 87.5% sensitivity on average. 

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5. In-situ particle analysis with heterogeneous background: a machine learning approach

   https://doi.org/10.1038/s41598-024-59558-7  

   Alam, Adeeb Ibne; Rahman, Md Hafizur; Zia, Akhter; Lowry, Nate; Chakraborty, Prabuddha; Hassan, Md Rafiul; Khoda, Bashir (May 2024, Scientific Reports)

   Abstract We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle–substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle–substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing. 

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