A photosensor employing data-driven binning for ultrafast image recognition

• URL: http://arxiv.org/abs/2111.10612v1
• Date: Sat, 20 Nov 2021 15:38:39 GMT
• Title: A photosensor employing data-driven binning for ultrafast image recognition
• Authors: Lukas Mennel, Aday J. Molina-Mendoza, Matthias Paur, Dmitry K. Polyushkin, Dohyun Kwak, Miriam Giparakis, Maximilian Beiser, Aaron Maxwell Andrews, Thomas Mueller
• Abstract summary: Pixel binning is a technique widely used in optical image acquisition and spectroscopy. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single superpixel. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pixel binning is a technique, widely used in optical image acquisition and spectroscopy, in which adjacent detector elements of an image sensor are combined into larger pixels. This reduces the amount of data to be processed as well as the impact of noise, but comes at the cost of a loss of information. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single superpixel that extends over the whole face of the chip. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm. We demonstrate the classification of optically projected images from the MNIST dataset on a nanosecond timescale, with enhanced sensitivity and without loss of classification accuracy. Our concept is not limited to imaging alone but can also be applied in optical spectroscopy or other sensing applications.

Related papers

• bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction [57.199618102578576]
  We propose bit2bit, a new method for reconstructing high-quality image stacks at original resolution from sparse binary quantatemporal image data. Inspired by recent work on Poisson denoising, we developed an algorithm that creates a dense image sequence from sparse binary photon data. We present a novel dataset containing a wide range of real SPAD high-speed videos under various challenging imaging conditions.
  arXiv  Detail & Related papers  (2024-10-30T17:30:35Z)
• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• Scattering-induced entropy boost for highly-compressed optical sensing and encryption [7.502671257653539]
  Image sensing often relies on a high-quality machine vision system with a large field of view and high resolution. We propose a novel image-free sensing framework for resource-efficient image classification. The proposed framework is shown to obtain over a 95% accuracy at sampling rates of 1% and 5% for classification on the MNIST dataset.
  arXiv  Detail & Related papers  (2022-12-16T09:00:42Z)
• Probabilistic Deep Metric Learning for Hyperspectral Image Classification [91.5747859691553]
  This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
  arXiv  Detail & Related papers  (2022-11-15T17:57:12Z)
• All-optical image classification through unknown random diffusers using a single-pixel diffractive network [13.7472825798265]
  classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields. Recent deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image sensor. Here, we present an all-optical processor to directly classify unknown objects through unknown, random phase diffusers using broadband illumination detected with a single pixel.
  arXiv  Detail & Related papers  (2022-08-08T08:26:08Z)
• Multispectral Satellite Data Classification using Soft Computing Approach [5.3971200250581814]
  We propose a grid-density based clustering technique for identification of objects. We introduce an approach to classify a satellite image data using a rule induction based machine learning algorithm.
  arXiv  Detail & Related papers  (2022-03-21T17:25:09Z)
• Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image Recognition [124.80263629921498]
  We propose Pixel Distillation that extends knowledge distillation into the input level while simultaneously breaking architecture constraints. Such a scheme can achieve flexible cost control for deployment, as it allows the system to adjust both network architecture and image quality according to the overall requirement of resources.
  arXiv  Detail & Related papers  (2021-12-17T14:31:40Z)
• Convolutional Deep Denoising Autoencoders for Radio Astronomical Images [0.0]
  We apply a Machine Learning technique known as Convolutional Denoising Autoencoder to denoise synthetic images of state-of-the-art radio telescopes. Our autoencoder can effectively denoise complex images identifying and extracting faint objects at the limits of the instrumental sensitivity.
  arXiv  Detail & Related papers  (2021-10-16T17:08:30Z)
• Pixel super-resolution using spatially-entangled photon pairs [2.4026706705044183]
  We introduce a pixel super-resolution technique based on measuring the full spatially-resolved joint probability distribution of photons. We demonstrate its use in various quantum imaging protocols using photon pairs, including quantum illumination, entanglement-enabled quantum holography, and in a full-field version of N00N-state quantum holography.
  arXiv  Detail & Related papers  (2021-05-21T13:43:51Z)
• Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems [56.154190098338965]
  We show that our proposed time multiplexed coded aperture (TMCA) can be optimized end-to-end. TMCA induces better coded snapshots enabling superior reconstructions in two different applications: compressive light field imaging and hyperspectral imaging. This codification outperforms the state-of-the-art compressive imaging systems by more than 4dB in those applications.
  arXiv  Detail & Related papers  (2021-04-06T22:42:34Z)
• Exploiting Raw Images for Real-Scene Super-Resolution [105.18021110372133]
  We study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images. We propose a method to generate more realistic training data by mimicking the imaging process of digital cameras. We also develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images.
  arXiv  Detail & Related papers  (2021-02-02T16:10:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.