A Data-Driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes

• URL: http://arxiv.org/abs/2403.10206v1
• Date: Fri, 15 Mar 2024 11:15:06 GMT
• Title: A Data-Driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes
• Authors: Peng Jia, Chao Lv, Yushan Li, Yongyang Sun, Shu Niu, Zhuoxiao Wang,
• Abstract summary: We introduce a data-driven framework for mitigating dark current noise and bad pixels for CMOS cameras. Our approach involves two key steps: pixel clustering and function fitting. Results show a considerable improvement in the detection efficiency of space-based telescopes.
• Score: 2.4489471766462625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been a gradual increase in the performance of Complementary Metal Oxide Semiconductor (CMOS) cameras. These cameras have gained popularity as a viable alternative to charge-coupled device (CCD) cameras in a wide range of applications. One particular application is the CMOS camera installed in small space telescopes. However, the limited power and spatial resources available on satellites present challenges in maintaining ideal observation conditions, including temperature and radiation environment. Consequently, images captured by CMOS cameras are susceptible to issues such as dark current noise and defective pixels. In this paper, we introduce a data-driven framework for mitigating dark current noise and bad pixels for CMOS cameras. Our approach involves two key steps: pixel clustering and function fitting. During pixel clustering step, we identify and group pixels exhibiting similar dark current noise properties. Subsequently, in the function fitting step, we formulate functions that capture the relationship between dark current and temperature, as dictated by the Arrhenius law. Our framework leverages ground-based test data to establish distinct temperature-dark current relations for pixels within different clusters. The cluster results could then be utilized to estimate the dark current noise level and detect bad pixels from real observational data. To assess the effectiveness of our approach, we have conducted tests using real observation data obtained from the Yangwang-1 satellite, equipped with a near-ultraviolet telescope and an optical telescope. The results show a considerable improvement in the detection efficiency of space-based telescopes.

Related papers

• EvMAPPER: High Altitude Orthomapping with Event Cameras [58.86453514045072]
  This work introduces the first orthomosaic approach using event cameras. In contrast to existing methods relying only on CMOS cameras, our approach enables map generation even in challenging light conditions.
  arXiv  Detail & Related papers  (2024-09-26T17:57:15Z)
• CamLoPA: A Hidden Wireless Camera Localization Framework via Signal Propagation Path Analysis [59.86280992504629]
  CamLoPA is a training-free wireless camera detection and localization framework. It operates with minimal activity space constraints using low-cost commercial-off-the-shelf (COTS) devices. It achieves 95.37% snooping camera detection accuracy and an average localization error of 17.23, under the significantly reduced activity space requirements.
  arXiv  Detail & Related papers  (2024-09-23T16:23:50Z)
• Simultaneous temperature estimation and nonuniformity correction from multiple frames [0.0]
  Low-cost microbolometer-based IR cameras are prone to spatially nonuniformity and to drift in temperature measurements. We propose a novel approach for simultaneous temperature estimation and nonuniformity correction (NUC) from multiple frames captured by low-cost microbolometer cameras.
  arXiv  Detail & Related papers  (2023-07-23T11:28:25Z)
• Polarimetric Imaging for Perception [3.093890460224435]
  We analyze the potential for improvement in perception tasks when using an RGB-polarimetric camera. We show that a quantifiable improvement can be achieved for both of them using state-of-the-art deep neural networks.
  arXiv  Detail & Related papers  (2023-05-24T06:42:27Z)
• Infrared photon-number-resolving imager using a Skipper-CCD [0.0]
  In astronomical detection, the search for habitable exoplanets demands imaging in the infrared its atmospheres looking for biosignatures. Here we report an imaging device in the infrared wavelength range able to arbitrarily reduce the readout noise. This device allows us to image objects in a broad range of intensities within the same frame and, by reducing the readout noise to less than 0.2e$-$, to distinguish even those shapes with less than two photons per pixel, unveiling what was previously hidden in the noise.
  arXiv  Detail & Related papers  (2023-01-26T01:22:41Z)
• CubeSat in-orbit validation of in-situ performance by high fidelity radiation modelling [55.41644538483948]
  The SpooQy-1 CubeSat mission demonstrated polarization-based quantum entanglement correlations using avalanche photodiodes for single-photon detection. We report the increasing dark count rates of two silicon Geiger-mode avalanche photodiodes observed throughout its 2 year orbital lifetime. We implement a high-fidelity radiation model combined with 3D computer aided design models of the SpooQy-1 CubeSat to estimate the accumulated displacement damage dose in each photodiode.
  arXiv  Detail & Related papers  (2022-09-01T12:33:27Z)
• Dense Optical Flow from Event Cameras [55.79329250951028]
  We propose to incorporate feature correlation and sequential processing into dense optical flow estimation from event cameras. Our proposed approach computes dense optical flow and reduces the end-point error by 23% on MVSEC.
  arXiv  Detail & Related papers  (2021-08-24T07:39:08Z)
• SE-Harris and eSUSAN: Asynchronous Event-Based Corner Detection Using Megapixel Resolution CeleX-V Camera [9.314068908300285]
  Event cameras generate an asynchronous event stream of per-pixel intensity changes with precise timestamps. We propose a corner detection algorithm, eSUSAN, inspired by the conventional SUSAN (smallest univalue segment assimilating nucleus) algorithm for corner detection. We also propose the SE-Harris corner detector, which uses adaptive normalization based on exponential decay to quickly construct a local surface of active events.
  arXiv  Detail & Related papers  (2021-05-02T14:06:28Z)
• Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array [0.0]
  New deep learning techniques present promising new analysis methods for Imaging Atmospheric Cherenkov Telescopes (IACTs) CNNs could provide a direct event classification method that uses the entire information contained within the Cherenkov shower image.
  arXiv  Detail & Related papers  (2021-03-10T13:54:43Z)
• Learning Camera Miscalibration Detection [83.38916296044394]
  This paper focuses on a data-driven approach to learn the detection of miscalibration in vision sensors, specifically RGB cameras. Our contributions include a proposed miscalibration metric for RGB cameras and a novel semi-synthetic dataset generation pipeline based on this metric. By training a deep convolutional neural network, we demonstrate the effectiveness of our pipeline to identify whether a recalibration of the camera's intrinsic parameters is required or not.
  arXiv  Detail & Related papers  (2020-05-24T10:32:49Z)
• SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing [131.04304632759033]
  Small and cluttered objects are common in real-world which are challenging for detection. In this paper, we first innovatively introduce the idea of denoising to object detection. Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects.
  arXiv  Detail & Related papers  (2020-04-28T06:03:54Z)

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.