Dr. Jose de Jesus Rangel-Magdaleno Profile

Dr. Jose de Jesus Rangel-Magdaleno

at Instituto Nacional de Astrofísica, Óptica y Electrónica

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Publications (5)

Proceedings Article | 14 November 2021 Poster + Paper

2D/3D single-pixel NIR image reconstruction method for outdoor applications in presence of rain

C. Osorio Quero, D. Durini Romero, J. Rangel-Magdaleno, J. Martinez Carranza, R. Ramos-Garcia

Proceedings Volume 11914, 1191415 (2021) https://doi.org/10.1117/12.2601118

KEYWORDS: Reconstruction algorithms, 3D image reconstruction, 3D image processing, Scattering, 3D modeling, Light scattering, Reflectivity, Particles, Mie scattering, Sensors

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In this paper, we propose a method for single-pixel 2D/3D imaging to be applied in hyperspectral cameras operating in outdoor applications in unmanned aerial vehicles (UAVs). In rain rich environments, if camera systems operating in the visible range are used, alongside the atmospheric absorption that inevitably takes place, additional interactions occur between the light to be detected and the raindrops that produce Rayleigh and other scattering effects, thus limiting the visibility of the imagers mainly due to induced limitations in what radiation propagation is concerned. To solve this problem, we follow the approach that consists in using InGaAs based single photodetectors or line sensors that operate in the near-infrared (NIR) part of the spectra, in the wavelength range between 905 nm and 1600 nm. Due to the high costs of the few commercially available InGaAs based image sensors, we propose implementing a vision system based on the principle of single-pixel imaging (SPI) using active NIR illumination as a low-cost solution for image sensors in low-light scattering media. The SPI approach allows for the reconstruction of images from the electrical response signals of single InGaAs photodetectors to impinging active NIR illumination patterns created by an array of NIR LEDs. The latter is enabled by applying compression sensing (CS) algorithms. In this work, we aim at improving the quality of the reconstructed 2D images, defining the SSIM > 0.6 and RMSE<0.3 in rain rich scenarios as goal specifications. This SPI approach is additionally used to generate 3D images by applying Shape-From-Shading (SPS) and improving mesh 3D algorithms. By projecting Hadamard active illumination patterns analyzed in different regions of the reconstructed image, we enable scanning Spiral and Hilbert traces for image reconstruction to improve the SSIM level and reduce RMSE level of the reconstructed 2D/3D image obtained in the presence of rain. For testing purposes, we developed a controlled characterization system that enables rain simulation to evaluate the quality of the reconstructed 2D/3D single-pixel images.

Proceedings Article | 4 May 2020 Presentation + Paper

Hardware parallel architecture proposed to accelerate the orthogonal matching pursuit compressive sensing reconstruction

C. Osorio Quero, D. Durini, R. Ramos-Garcia, J. Rangel-Magdaleno, J. Martinez-Carranza

Proceedings Volume 11396, 113960N (2020) https://doi.org/10.1117/12.2558937

KEYWORDS: Reconstruction algorithms, Image processing, Detection and tracking algorithms, Image restoration, Video acceleration, Video, Compressed sensing, Chemical species, Signal to noise ratio, Signal processing

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The compressive sensing (CS) technique is a novel tool used to reconstruct images using fewer samples, normally sparse in the transform domain, than those required by conventional imaging systems. However, the methods applied for signal reconstruction within the CS approach still present some problems in the implementation, mainly due to their intensive computational demand and high power consumption requirements. These drawbacks need addressing if this approach is followed in systems aimed at e.g. drone autonomous flying or other embedded applications that additionally require very short processing times. In this paper we evaluate the use of hardware based parallel processing architecture for the implementation of the Orthogonal Matching Pursuit (OMP) algorithm, one of the most efficient CS reconstruction algorithms developed so far. To improve the algorithm performance, we target different maximum allowed processing times to reach minimum image resolutions required by each system of interest using different sparse (16 and 64) amounts of single-pixel generated samples per image. We also target the final image resolution to be above 20 dB in terms of the peak signal-to-noise ratio (PSNR). To reduce the execution and processing times required to generate each image, we propose implementing parallel kernels in the hardware platform for each of the operations required by the algorithms under study. In the proposed implementation the reconstructed images are used to generate video streams that form the foundation on which decisions are to be made by the system in continuous time, whereby each single image (frame) reconstruction cannot overcome 30 ms in order to maintain the minimum amount of frames per second (fps) above 33 (minimum required for an acceptable video stream). The implementation of a variation of the OMP algorithm in a graphics processing unit (GPU) using parallel architecture approach allows obtaining processing times 4 or 5 times shorter than those obtained if central processing unit (CPU) based architecture implementation is used for the same purpose.

Proceedings Article | 28 April 2020 Paper

Implementation of a hyperspectral integrated vision system combining radar technology and single-pixel optical principle for unmanned ground vehicles (UGV)

C. Osorio Quero, D. Durini, R. Ramos-García, J. Rangel-Magdaleno, J. Martinez-Carranza, J. L. Olvera, A. Corona

Proceedings Volume 11408, 1140815 (2020) https://doi.org/10.1117/12.2558929

KEYWORDS: Radar, Imaging systems, Photodetectors, Light emitting diodes, Indium gallium arsenide, Orthogonal frequency division multiplexing, Sensors, Near infrared, Spatial resolution, Ranging

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During the last decades the radio detecting and ranging (RADAR) technology underwent an evolution transiting from the linear-frequency-modulated (LFM) systems developed in the 1970s, up to the orthogonal frequency-division multiplexing (OFDM) systems developed in the early 2000s. In mid 2010s, systems were proposed that combined the radar principle with optical solutions developed for imaging and ranging tasks following a hyperspectral embedded systems approach. The idea was to profit on the one side from the possibility offered by RADAR systems to work in harsh environments using emitted radio waves and detect mainly metal objects placed far away (hundreds of meters or even kilometers) from the detection system with positioning spatial resolutions in tens of centimeters, even if there are non-metallic barriers such as e.g. walls in between; and expand this possibility by using optical systems (e.g. light detecting and ranging –LIDAR- systems), using visible light active illumination, capable of generating 2D and 3D images of objects placed at much smaller distances from the detector, but allowing for much higher spatial resolutions (in the millimeter range). To reduce the atmospheric absorption of the emitted active illumination and increase the emitted optical power allowed for these systems that can correctly function even in harsh environments, we propose shifting the active illumination wavelengths from the visible range to the near infra-red (NIR) range, e.g. to 1550 nm. Lacking affordable image sensors fabricated in InGaAs technology, capable of detecting NIR radiation, in this paper we propose a hyperspectral imaging system using a very low power consuming single commercially available InGaAs photodiode to generate 2D images using the single-pixel imaging (SPI) approach based on compressive sensing (CS) and an array of NIR light emitting LEDs, combined with an 80 GHz millimeter band RADAR. The system is conceived to deliver a maximum radar range of 150 m with a maximum spatial resolution of ≤ 5 cm and a RADAR cross-section (RCS) of 10 – 50 m², combined with an optical system capable of generating 24 fps video streams based on SPI generated images yielding a maximum ranging depth of 10 m with a spatial resolution of < 1 cm. The proposed system will be used in unmanned ground vehicle (UGV) applications enabling decision making in continuous time. The power consumption, dimensions and weight of the hyperspectral ranging system will be adjusted to the UGV targeted applications.

Proceedings Article | 27 April 2020 Paper

Evaluation of a 3D imaging vision system based on a single-pixel InGaAs detector and the time-of-flight principle for drones

C. Osorio Quero, D. Durini, R. Ramos-Garcia, J. Rangel-Magdaleno, J. Martinez-Carranza

Proceedings Volume 11402, 114020T (2020) https://doi.org/10.1117/12.2558918

KEYWORDS: Distance measurement, Indium gallium arsenide, Imaging systems, Near infrared, Photodiodes, Sensors, Image processing, Photodetectors, Spatial resolution, Light emitting diodes

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SPIE Journal Paper | 12 June 2017 Open Access

Use of kurtosis for locating deep blood vessels in raw speckle imaging using a homogeneity representation

Hayde Peregrina-Barreto, Elizabeth Perez-Corona, Jose Rangel-Magdaleno, Ruben Ramos-Garcia, Roger Chiu, Julio Ramirez-San-Juan

JBO, Vol. 22, Issue 06, 066004, (June 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.6.066004

KEYWORDS: Blood vessels, Speckle imaging, Speckle, Biological research, Blood circulation, Visual analytics, Visualization, Tissues, Laser speckle imaging, Tissue optics

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