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1. Estimation of Background Medium’s Properties in Microwave Holographic Imaging

   https://doi.org/10.1109/WAMICON47156.2021.9443615  

   Wu, Hailun; Amineh, Reza K. (April 2021, 2021 IEEE 21st Annual Wireless and Microwave Technology Conference (WAMICON))

   null (Ed.)

   In this paper, an approach is proposed to enhance the images produced by holographic microwave imaging technique. The approach estimates the electrical properties of the background medium. For that, first, we assumed that the background properties are within a pre-known range. Then, wideband holographic imaging technique is applied to two sets of frequencies to reconstruct two sets of images for assumed property values within the pre-determined range. An error is computed to record the differences between the two sets of images. The error is expected to be minimum at the true values of the background medium’s properties. Simulation and experimental results demonstrate the validity of the proposed technique. 

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2. Semi-heuristic phase compensation in digital holographic microscopy for stable and accurate quantitative phase imaging of moving objects

   https://doi.org/10.1016/j.optlaseng.2023.107937  

   Obando-Vásquez, Sofía; Doblas, Ana; Trujillo, Carlos (March 2024, Optics and Lasers in Engineering)

   Digital holographic microscopy (DHM) is a cutting-edge interferometric technique to recover the complex wavefield scattered by microscopic samples from digitally recorded intensity patterns. In off-axis DHM, the challenge is digitally generating the reference wavefront replica to compensate for the tilt between the interfering waves. Current methods to estimate the reference wavefront's parameters rely on brute-force grid searches or heuristics algorithms. Whereas brute-forced searches are time-consuming and impractical for video-rate quantitative phase imaging and analysis, applying heuristics methods in holographic videos is limited since the phase background level occasionally changes between frames. A semi-heuristic phase compensation (SHPC) algorithm is proposed to address these challenges to reconstruct phase images with minimum distortion in the full field-of-view (FOV) from holograms recorded by a telecentric off-axis digital holographic microscope. The method is tested with a USAF test target, smearing red blood cells and alive human sperm. The SHPC method provides accurate phase maps as the reference brute-force method but with a 92-fold reduction in processing time. Furthermore, this method was tested for reconstructing experimental holographic videos of dynamic specimens, obtaining stable phase values and minimal differences in the background between frames. This proposed method provides state-of-the-art phase reconstructions with high accuracy and stability in holographic videos, allowing the successful XYZ tracking of single-moving sperm cells. 

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3. Combined speckle- and propagation-based single shot two-dimensional phase retrieval method

   https://doi.org/10.1364/OE.531269  

   Leong, Andrew_F_T; Hodge, Daniel_S; Kurzer-Ogul, Kelin; Marchesini, Stefano; Pandolfi, Silvia; Liu, Yanwei; Barber, John_L; Li, Kenan; Sakdinawat, Anne; Galtier, Eric_C; et al (December 2024, Optics Express)

   Single-shot two-dimensional (2D) phase retrieval (PR) can recover the phase shift distribution within an object from a single 2D x-ray phase contrast image (XPCI). Two competing XPCI imaging modalities often used for single-shot 2D PR to recover material properties critical for predictive performance capabilities are: speckle-based (SP-XPCI) and propagation-based (PB-XPCI) XPCI imaging. However, PR from SP-XPCI and PB-XPCI images are, respectively, limited to reconstructing accurately slowly and rapidly varying features due to noise and differences in their contrast mechanisms. Herein, we consider a combined speckle- and propagation-based XPCI (SPB-XPCI) image by introducing a mask to generate a reference pattern and imaging in the near-to-holographic regime to induce intensity modulations in the image. We develop a single-shot 2D PR method for SPB-XPCI images of pure phase objects without imposing restrictions such as object support constraints. It is compared against PR methods inspired by those developed for SP-XPCI and PB-XPCI on simulated and experimental images of a thin glass shell before and during shockwave compression. Reconstructed phase maps show improvements in quantitative scores of root-mean-square error and structural similarity index measure using our proposed method. 

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4. CNN-Based LCD Transcription of Blood Pressure From a Mobile Phone Camera

   https://doi.org/10.3389/frai.2021.543176  

   Kulkarni, Samruddhi S.; Katebi, Nasim; Valderrama, Camilo E.; Rohloff, Peter; Clifford, Gari D. (May 2021, Frontiers in Artificial Intelligence)

   null (Ed.)

   Routine blood pressure (BP) measurement in pregnancy is commonly performed using automated oscillometric devices. Since no wireless oscillometric BP device has been validated in preeclamptic populations, a simple approach for capturing readings from such devices is needed, especially in low-resource settings where transmission of BP data from the field to central locations is an important mechanism for triage. To this end, a total of 8192 BP readings were captured from the Liquid Crystal Display (LCD) screen of a standard Omron M7 self-inflating BP cuff using a cellphone camera. A cohort of 49 lay midwives captured these data from 1697 pregnant women carrying singletons between 6 weeks and 40 weeks gestational age in rural Guatemala during routine screening. Images exhibited a wide variability in their appearance due to variations in orientation and parallax; environmental factors such as lighting, shadows; and image acquisition factors such as motion blur and problems with focus. Images were independently labeled for readability and quality by three annotators (BP range: 34–203 mm Hg) and disagreements were resolved. Methods to preprocess and automatically segment the LCD images into diastolic BP, systolic BP and heart rate using a contour-based technique were developed. A deep convolutional neural network was then trained to convert the LCD images into numerical values using a multi-digit recognition approach. On readable low- and high-quality images, this proposed approach achieved a 91% classification accuracy and mean absolute error of 3.19 mm Hg for systolic BP and 91% accuracy and mean absolute error of 0.94 mm Hg for diastolic BP. These error values are within the FDA guidelines for BP monitoring when poor quality images are excluded. The performance of the proposed approach was shown to be greatly superior to state-of-the-art open-source tools (Tesseract and the Google Vision API). The algorithm was developed such that it could be deployed on a phone and work without connectivity to a network. 

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5. Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion

   https://doi.org/10.3390/ma17020510  

   Dong, William; Lian, Jason; Yan, Chengpo; Zhong, Yiran; Karnati, Sumanth; Guo, Qilin; Chen, Lianyi; Morgan, Dane (January 2024, Materials)

   In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets. 

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