Passive imaging of single photon flux: strategies for noise treatment, motion blur reduction and super-resolution up-scaling

AbstractImaging across both the full transverse spatial and temporal dimensions of a scene with high precision in all three coordinates is key to applications ranging from LIDAR to fluorescence lifetime imaging. However, compromises that sacrifice, for example, spatial resolution at the expense of temporal resolution are often required, in particular when the full 3-dimensional data cube is required in short acquisition times. We introduce a sensor fusion approach that combines data having low-spatial resolution but high temporal precision gathered with a single-photon-avalanche-diode (SPAD) array with data that has high spatial but no temporal resolution, such as that acquired with a standard CMOS camera. Our method, based on blurring the image on the SPAD array and computational sensor fusion, reconstructs time-resolved images at significantly higher spatial resolution than the SPAD input, upsampling numerical data by a factor $$12 \times 12$$ 12 × 12 , and demonstrating up to $$4 \times 4$$ 4 × 4 upsampling of experimental data. We demonstrate the technique for both LIDAR applications and FLIM of fluorescent cancer cells. This technique paves the way to high spatial resolution SPAD imaging or, equivalently, FLIM imaging with conventional microscopes at frame rates accelerated by more than an order of magnitude.

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Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system

Nuclear Engineering and Technology ◽

10.1016/j.net.2021.01.011 ◽

2021 ◽

Author(s):

Kyuseok Kim ◽

Youngjin Lee

Keyword(s):

Imaging System ◽

Single Photon ◽

Photon Emission ◽

Super Resolution ◽

Emission Computed Tomography ◽

Computed Tomography Imaging ◽

Image Super Resolution ◽

Single Photon Emission Computed ◽

Single Image Super Resolution ◽

Photon Emission Computed Tomography

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High Dynamic Range Imaging with TDC-Based CMOS SPAD Arrays

Instruments ◽

10.3390/instruments3030038 ◽

2019 ◽

Vol 3 (3) ◽

pp. 38 ◽

Cited By ~ 1

Author(s):

Majid Zarghami ◽

Leonardo Gasparini ◽

Matteo Perenzoni ◽

Lucio Pancheri

Keyword(s):

Dynamic Range ◽

Single Photon ◽

Photon Counting ◽

Complementary Metal Oxide Semiconductor ◽

High Dynamic Range ◽

Oxide Semiconductor ◽

Photon Flux ◽

Integration Time ◽

Rate Dependent ◽

High Dynamic

This paper investigates the use of image sensors based on complementary metal–oxide–semiconductor (CMOS) single-photon avalanche diodes (SPADs) in high dynamic range (HDR) imaging by combining photon counts and timestamps. The proposed method is validated experimentally with an SPAD detector based on a per-pixel time-to-digital converter (TDC) architecture. The detector, featuring 32 × 32 pixels with 44.64-µm pitch, 19.48% fill factor, and time-resolving capability of ~295-ps, was fabricated in a 150-nm CMOS standard technology. At high photon flux densities, the pixel output is saturated when operating in photon-counting mode, thus limiting the DR of this imager. This limitation can be overcome by exploiting the distribution of photon arrival times in each pixel, which shows an exponential behavior with a decay rate dependent on the photon flux level. By fitting the histogram curve with the exponential decay function, the extracted time constant is used to estimate the photon count. This approach achieves 138.7-dB dynamic range within 30-ms of integration time, and can be further extended by using a timestamping mechanism with a higher resolution.

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Modified Rider Optimization-Based V Channel Magnification for Enhanced Video Super Resolution

International Journal of Image and Graphics ◽

10.1142/s0219467821500030 ◽

2021 ◽

pp. 2150003

Author(s):

Rohita H. Jagdale ◽

Sanjeevani K. Shah

Keyword(s):

Spline Interpolation ◽

Super Resolution ◽

Window Size ◽

Cubic Spline ◽

Motion Blur ◽

Video Frame ◽

High Dimensional ◽

Cubic Spline Interpolation ◽

Dimensional Matrix ◽

Video Frames

In video Super Resolution (SR), the problem of cost expense concerning the attainment of enhanced spatial resolution, computational complexity and difficulties in motion blur makes video SR a complex task. Moreover, maintaining temporal consistency is crucial to achieving an efficient and robust video SR model. This paper plans to develop an intelligent SR model for video frames. Initially, the video frames in RGB format will be transformed into HSV. In general, the improvement in video frames is done in V-channel to achieve High-Resolution (HR) videos. In order to enhance the RGB pixels, the current window size is enhanced to high-dimensional window size. As a novelty, this paper intends to formulate a high-dimensional matrix with enriched pixel intensity in V-channel to produce enhanced HR video frames. Estimating the enriched pixels in the high-dimensional matrix is complex, however in this paper, it is dealt in a significant way by means of a certain process: (i) motion estimation (ii) cubic spline interpolation and deblurring or sharpening. As the main contribution, the cubic spline interpolation process is enhanced via optimization in terms of selecting the optimal resolution factor and different cubic spline parameters. For optimal tuning, this paper introduces a new modified algorithm, which is the modification of the Rider Optimization Algorithm (ROA) named Mean Fitness-ROA (MF-ROA). Once the HR image is attained, it combines the HSV and converts to RGB, which obtains the enhanced output RGB video frame. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to BRISQUE, SDME and ESSIM measures, and proves its superiority over other models.

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