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Dark-frame subtraction

About: Dark-frame subtraction is a research topic. Over the lifetime, 1216 publications have been published within this topic receiving 20763 citations.


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Proceedings ArticleDOI
03 Feb 2017
TL;DR: In this paper, a comprehensive model for staring array simulation is described, which covers all effects from photon signal generation through to detection and processing in the staring array sensor, and is implemented in Python and in C++.
Abstract: A comprehensive model for staring array simulation is described. The model covers all effects from photon signal generation through to detection and processing in the staring array sensor. The model follows the signal flow from photon generation, through a staring focal plane array (FPA) from the detector, through several conversions in the read out integrated circuit (ROIC) and finally conversion to a digital signal. Spatial nonuniformity modeling for photoresponse, dark current generation and source follower offset is included. The list of noise sources includes: photon noise, quantum conversion uncertainty, dark noise, kTC noise, source follower noise and quantization noise. Several components with (simplified) nonlinear responses are also modeled: sense node capacitance variation with charge, source follower nonlinearity and nonlinearity in the digital conversion. The code implementations take images as input, applying the various processes independently on individual pixels (e.g., shot noise) or on complete images (e.g., spatial nonuniformity). Some noise sources vary temporally across frames (shot, thermal, kTC) while other noise sources are fixed across frames (fixed pattern noises). The application of the model is demonstrated by tracing the signal path from source to sensor output, with intermediate results along the path. The model is implemented in Python (as part of the pyradi open source computational radiometry module) and in a C++ image simulation. The purpose with this work is to predict what the performance of a given sensor will be in terms of image appearance, given the devices specifications and key design parameters. The execution of this work lead to the important recommendation that nonuniformity correction for infrared sensors should be performed at well fill levels corresponding to the minimum and maximum in the scene, not to fixed percentage levels in the charge well. The objective with this work is to provide a `generic' model that can be adapted by adjusting model parameters. For more accurate modeling of specific sensors, dedicated models should be developed, but for all but the most demanding requirement, this model should be adequate in scope of detail and freedom of characteristics.

6 citations

Patent
Gi-Beom Kim1
06 Mar 1992
TL;DR: In this paper, a circuit for eliminating a ghost noise of an image processing system in transmitting an image signal was proposed, wherein an image data and a noise are separated from each other visually and the ghost noise added during transmitting is changed to a white noise so as to reduce an extent of deterioration of a picture quality.
Abstract: A circuit for eliminating a ghost noise of an image processing system in transmitting an image signal, wherein an image data and a noise are separated from each other visually and the ghost noise added during transmitting is changed to a white noise so as to reduce an extent of deterioration of a picture quality because a pattern of a data scrambled at each frame is changed by an initial value being different at each frame.

6 citations

Proceedings ArticleDOI
01 Dec 2012
TL;DR: The results show that the proposed technique preserves the finer edge details of the image during the impulse noise removal process, and proves that the design is of low computational complexity and lesser hardware cost.
Abstract: Impulse noise is caused by malfunctioning pixels in camera sensors, faulty memory locations in hardware, or transmission in a noisy channel Several distortions limit the quality of digital images during image acquisition, formation, storage and transmission Impulse noise is introduced in the images from some digital sources due to acquisition error or transmission error or a problem in the ground processing systems In this paper, an efficient edge preserving impulse noise removal technique has been proposed The algorithm has been simulated on MATLAB and implemented using FPGA The results show that the proposed technique preserves the finer edge details of the image during the impulse noise removal process The technique has high performance in terms of qualitative analysis as well as visual quality using PSNR and MAE Also, synthesis results prove that the design is of low computational complexity and lesser hardware cost

6 citations

Patent
24 Apr 2006
TL;DR: In this article, a noise reducing device was proposed to reduce non-correlative random noise in the plural blackout image data by capturing a field with an image capturing part under a light shielded state.
Abstract: A noise reducing device captures image data obtained by capturing a field with an image capturing part and a plurality of blackout image data obtained by capturing the field with the image capturing part under a light shielded state. This device reduces non-correlative random noise in the plural blackout image data. With random noise reduced, fixed pattern noise appears more accurately in resultant as blackout image data B. This device reduces the fixed pattern noise in the image data by using this blackout image data B.

6 citations

Book ChapterDOI
01 Jan 2003
TL;DR: Noise in an image can be defined as the unwanted part of that image, which need not be independent of, but can be closely connected with, the wanted signal itself, and if the signal is removed, the noise will change.
Abstract: Noise in an image can be defined as the unwanted part of that image. The noise may be random in some way, as is the pepper-and-salt appearance on a television screen when the station goes off the air, or it may be systematic, as with the ghost seen when an echo of the wanted signal arrives with a time delay after reflection from a hill. When the television image responds independently to sparks in a faulty thermostat in the nearby refrigerator or to a faulty ignition system on a passing motorcycle, the noise exhibits both random and systematic features. In other cases, the wanted signal may be random; thermal microwave or infrared radiation used for mapping the ground is of this nature. As a result, one person’s noise may be another person’s signal and vice versa. Very often it does not matter much what the character of the noise is, only its magnitude is needed, an attitude that is reflected in the term signal-to-noise ratio. As the examples show, the noise in an image need not be independent of, but can be closely connected with, the wanted signal itself. In the latter case if the signal is removed, the noise will change. When the noise is independent, it may be studied on its own in the absence of any wanted signal.

6 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202221
20213
20202
20192
20187