EP2643967A1 - Procédé et dispositif d'évaluation de l'effet moustiquaire d'un dispositif d'enregistrement d'images - Google Patents

Procédé et dispositif d'évaluation de l'effet moustiquaire d'un dispositif d'enregistrement d'images

Info

Publication number
EP2643967A1
EP2643967A1 EP11769819.1A EP11769819A EP2643967A1 EP 2643967 A1 EP2643967 A1 EP 2643967A1 EP 11769819 A EP11769819 A EP 11769819A EP 2643967 A1 EP2643967 A1 EP 2643967A1
Authority
EP
European Patent Office
Prior art keywords
image
luminous intensity
intensity information
parameters
capture device
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP11769819.1A
Other languages
German (de)
English (en)
Inventor
Paul Ruhnau
Marc Geese
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Robert Bosch GmbH
Original Assignee
Robert Bosch GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert Bosch GmbH filed Critical Robert Bosch GmbH
Publication of EP2643967A1 publication Critical patent/EP2643967A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
    • H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
    • H04N25/67—Noise processing, e.g. detecting, correcting, reducing or removing noise applied to fixed-pattern noise, e.g. non-uniformity of response
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
    • H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
    • H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
    • H04N25/63—Noise processing, e.g. detecting, correcting, reducing or removing noise applied to dark current
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
    • H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
    • H04N25/67—Noise processing, e.g. detecting, correcting, reducing or removing noise applied to fixed-pattern noise, e.g. non-uniformity of response
    • H04N25/671—Noise processing, e.g. detecting, correcting, reducing or removing noise applied to fixed-pattern noise, e.g. non-uniformity of response for non-uniformity detection or correction

Definitions

  • the present invention relates to a method for estimating a screen-lattice effect of an image capture device, to a method for correcting a screen-lattice effect of an image capture device, and to a corresponding device.
  • fly screen effect is estimated during manufacture and additionally or alternatively in a separate laboratory environment.
  • the methods used are mainly based on the estimation of the optical flow or other closely associated methods and procedures.
  • the present invention provides a method for estimating a screen-mesh effect of an image capture device, an to correct a fly screen effect of an image capture device, further an apparatus that uses these methods and finally presented a corresponding computer program product according to the independent claims.
  • Advantageous embodiments result from the respective subclaims and the following description.
  • the invention is based on the recognition that the image information captured by an image capture device continuously changes due to a movement of the image capture device or the environment detected by the image capture device. If image information acquired over a period of time is averaged or added up, the resulting image information typically has a smooth course. Areas in which the smooth path is disturbed indicate disturbances caused by the image capture device. Since a real image capture device causes such disturbances, image information captured by an image capture device and the resulting image information generated from a plurality of acquired image information does not have a smooth course.
  • the perturbations caused by the image capture device can be described by corresponding parameters in a model description of the image capture device.
  • the parameters can be determined by applying the resulting image information according to the model description with parameters and looking for values for the parameters in which the resulting image information has the smoothest possible course.
  • captured image information can be applied to the parameters and subsequently the smoothness of the acquired image information can be determined.
  • the individual values for the smoothness of the acquired image information can be summarized as resulting smoothness and those values can be determined for the parameters in which the resulting smoothness is minimized.
  • the approach of the present invention describes an on-line method of insect screen effect estimation based on the analysis of image sequences.
  • some assumptions are made about the world and the capture of the frames.
  • the approach is suitable for a variety of video-based methods of single image capture in a natural environment.
  • this applies to video sequences of modern vehicle front cameras or cameras on mobile robots.
  • FPN FPN be corrected depending on the temperature. This also leads to cost savings in the manufacturing process.
  • Using an on-line FPN correction eliminates the step in the manufacturing process of measuring the fly screen effect and then storing it in the device. In this way, the storage space in the device can be reduced. This allows the use of less expensive imaging hardware due to the good correction techniques.
  • the inventive approach is based on the derivation of spatial and / or temporal properties of measured images or image sequences, in short image property.
  • This image property can be realized by the smoothness, but also by the change in brightness or a completely different global image properties.
  • Such an image characteristic can usually be mapped to a number.
  • assumptions about this image property are estimated for an ideal image or sequence of images. This estimate may e.g. derived theoretically or heuristically, that is to say from experience.
  • image correction parameters are now estimated which correct the image characteristic of the measured images in the direction of the ideal image property.
  • a minimization process now finds the ideal parameter set for the given input data. It does not have to be completely known the ideal images. Rather, it is sufficient to define an abstract image property of these images in such a way that the results obtained can be usefully used in real applications.
  • the inventive approach can be applied as follows.
  • As a global parameter of the measured images the smoothness of merged frames of a sequence is estimated, or alternatively, the smoothness of multiple frames is merged.
  • As an assumed ideal image an undisturbed, smooth image is assumed.
  • the correction parameters which correct the smoothness of the measured image for the assumed ideal smoothness of an ideal image, can also be described by a sensor model and thus also be identified with the fixed pattern noise.
  • the minimization process now finds an ideal parameter set for a given number of images. Are these pictures chosen representative and sufficient in number, so the FPN found, in the model also corresponds to the FPN of the image capture device.
  • the image capture device can be a camera or a part of a camera, for example a number of image sensors.
  • the image capture device can be arranged, for example, on a vehicle in order to detect an environment of the vehicle.
  • a light intensity can be detected by a pixel of the image capture device.
  • Several simultaneously or directly successively detected light intensities of a plurality of adjacent pixels can be combined to form a luminous intensity information.
  • a luminous intensity information can be an image or a recording of the image acquisition device or a partial region of a corresponding image or a corresponding image.
  • a luminous intensity information can be detected or provided by a region of a sensor surface of the image capture device. If a two-dimensional coordinate system is placed in the sensor surface, the light intensity information for different coordinates can have different light intensity values. Likewise, the plurality of parameters for different coordinates of the coordinate system can have different values. point.
  • the plurality of parameter values may be represented as a matrix of numbers, with each image sensor being assigned its own number in the form of a parameter value.
  • the plurality of luminous intensity information can be acquired one after the other, from one and the same area of the sensor surface. A number of luminous intensity information used may be dependent on a maximum possible resolution of a light intensity by a pixel of the image capture device. Thus, the number of luminous intensity information can be selected larger than the resolution.
  • the image capture device can be described by a model.
  • the model may define a relationship between real image information captured by the image capture device and the corresponding image information captured by the image capture device.
  • the model can be a mathematical function.
  • the at least one parameter may be a variable of the model.
  • the at least one parameter can be a disturbance variable of the model representing the fly screen effect.
  • the image information can be from the
  • Light intensity information and the plurality of parameters are determined by the light intensity information is merged and then acted upon with the plurality of parameters, or by the luminous intensity information each acted upon by the plurality of parameters and then merged.
  • the image property may represent a spatial and / or temporal property of the image information.
  • the ideal image characteristic can be estimated in advance for an ideal image or an ideal image sequence.
  • the picture property can be represented by a value or a number.
  • the approximation of the image property to the ideal image property may be performed based on a comparison between the ideal image property and a current image property determined with current parameter values. Several comparisons can be used to find those parameter values in which a deviation between the ideal image property and the current image property is minimal or lies within a tolerance window.
  • An example of a suitable image property is the
  • Smoothness of image information can mean the absence of high frequencies in terms of image information. Minimal smoothness is present with constant image information, which is not desirable because the image information is then erased.
  • the "desired" smoothness which can correspond to the ideal image quality, does not contain any of the disturbing parts of the image.These disturbing image components are usually represented by high spatial frequencies and occur in the real, unimportant image. disturbed image information is not available.
  • the individual parameter values of the plurality of parameter values may be selected such that the image property corresponds to the ideal image property or comes as close as possible to the ideal image property, for example within a predetermined value range around a value of the ideal image property.
  • the ideal image property is when the image information has as few high frequencies as possible.
  • a corresponding online estimation method for the fly screen effect can be carried out online, that is to say during operation of the image capture device in which the image capture device makes recordings of the surroundings.
  • the step of determining the image characteristic may comprise the steps of: merging the plurality of luminous intensity information to determine a resultant luminous intensity information; Applying the resulting luminous intensity information with the plurality of parameters to determine a luminous intensity information applied with the plurality of parameters; and determining the image characteristic as an image characteristic of the applied luminous intensity information.
  • Merging can be done by taking an average of the luminous intensity information or by adding up the luminous intensity information.
  • the merging can also be carried out in a completely different way, e.g. as a weighting of the past. Both the individual luminous intensity information and the resulting luminous intensity information have disturbances caused by the screen-lattice effect.
  • the disturbances caused by the screen-fly effect can be reduced.
  • the better the disturbances are represented by the plurality of parameters the better the disturbances in the applied luminous intensity information, using the plurality of parameters, can be reduced.
  • the better the interference is reduced the more trouble-free, for example smoother, is in turn the applied luminous intensity information.
  • the one of the plurality of parameter values is best suited for reducing the disturbance caused by the screen-lattice effect, which for example leads to an ideal smoothness of the applied luminous intensity information. In order not to achieve the greatest possible smoothness, which leads to constant images, the
  • Minimization rule also regularization parameters (alpha and beta) include. These can be chosen so that the smoothness after correction matches as much as possible with the assumed or experienced smoothness of the real information. According to an alternative embodiment, the step of determining the parameters (alpha and beta) include. These can be chosen so that the smoothness after correction matches as much as possible with the assumed or experienced smoothness of the real information. According to an alternative embodiment, the step of determining the parameters (alpha and beta) include. These can be chosen so that the smoothness after correction matches as much as possible with the assumed or experienced smoothness of the real information. According to an alternative embodiment, the step of determining the following parameters (alpha and beta) include. These can be chosen so that the smoothness after correction matches as much as possible with the assumed or experienced smoothness of the real information. According to an alternative embodiment, the step of determining the steps (alpha and beta) include. These can be chosen so that the smoothness after correction matches as much as possible with the assumed or experienced smoothness of the real information. According to an alternative embodiment, the step of determining the
  • Image characteristic comprising the steps of: each of the plurality of luminous intensity information being supplied with the plurality of parameters to determine a plurality of irradiated luminous intensity information; Determining each one of an image characteristic of the plurality of applied luminous intensity information; and determining the image characteristic by merging the image characteristics of the plurality of applied luminous intensity information.
  • a value of the image characteristic for example smoothness, can be determined by means of a suitable mathematical or logical function.
  • the merging of the values of the individual image properties can be carried out by averaging the values or by adding up the values.
  • a small amount of smoothness is very smooth and a large value of smoothness is not smooth, that is rough.
  • the at least one parameter may represent the Dark Signal Non-Uniformity (DSNU) and, additionally or alternatively, the Photo Response Non-Uniformity (PRNU) of the image capture device.
  • DSNU Dark Signal Non-Uniformity
  • PRNU Photo Response Non-Uniformity
  • all combinations of FPN parameters can be estimated.
  • the Photo Response Non-Uniformity can affect the characteristic noise of the image capture device.
  • the dark signal nonuniformity may relate to deviations of signal responses of individual sensor regions from an average value in the event that no light hits the image capture device.
  • the at least one parameter may include a factor and / or a summand with which a luminous intensity information to be detected by the image capture device is applied in accordance with the model in order to obtain a Determine modeled luminous intensity information taking into account the effect of a screened screen.
  • the parameter PRNU can be used as factor and the parameter DSNU as addend.
  • the plurality of parameter values of the plurality of parameters may be determined based on a minimization process. It is thus possible to determine a suitable parameter value for each of the plurality of parameters.
  • the plurality of parameter values may be determined as minima of a function describing the smoothness of the image information, the function comprising a derivation of the image information over at least one main extension direction of a detection surface of the image capture device.
  • a value of the image characteristic such as smoothness can be mathematically determined and evaluated.
  • the image property can represent a spatial and additionally or alternatively a temporal property of the image information.
  • the present invention further provides a method of correcting a screen-lattice effect of an image capture device, comprising the steps of:
  • the correction can be carried out by applying the plurality of parameters to the light information detected by the sensors, for example by multiplying the intensity information by a corresponding parameter value or by adding a corresponding parameter value.
  • the detected luminous intensity information can be corrected for the ascertained screen-lattice effect. This can cause a complete or partial correction of the existing fly screen effect.
  • the step of determining the plurality of parameter values may be repeatedly performed repeatedly.
  • the detected luminous intensity information can be applied to a last-determined plurality of parameter values. In this way, for example, could be addressed quickly on temperature-dependent changes.
  • the step of determining the parameter value may be performed in response to a predetermined value of a detected temperature of the image capture device or an environment of the image capture device.
  • the present invention further provides an apparatus adapted to perform the steps of the method according to the invention in corresponding devices. Also by this embodiment of the invention in the form of a control device, the object underlying the invention can be achieved quickly and efficiently.
  • a device can be understood as meaning an electrical device which processes sensor signals and outputs control signals in dependence thereon.
  • the device may have an interface, which may be formed in hardware and / or software.
  • the interfaces can be part of a so-called system ASIC, for example, which contains a wide variety of functions of the device.
  • the interfaces are their own integrated circuits or at least partially consist of discrete components.
  • the interfaces may be software modules that are present, for example, on a microcontroller in addition to other software modules.
  • Also of advantage is a computer program product with program code which can be stored on a machine-readable carrier such as a semiconductor memory, a hard disk memory or an optical memory and for carrying out the method according to one of the embodiments described above. is used when running the program on a device that corresponds to a computer.
  • a machine-readable carrier such as a semiconductor memory, a hard disk memory or an optical memory
  • FIG. 1 is a block diagram of an image capture device according to an embodiment of the present invention.
  • FIG. 2 is a block diagram of an apparatus for correcting a fly screen effect of an image capture device according to an embodiment of the present invention
  • FIG. 3 is a flowchart of a method for estimating a screen-mesh effect of an image capture device, according to an embodiment of the present invention.
  • FIG. 4 shows a flowchart of a further method for estimating a screen-mesh effect of an image capture device, according to an exemplary embodiment of the present invention.
  • Fig. 1 shows a block diagram of an image capture device according to an embodiment of the present invention.
  • the image capture device has an image sensor 102, which is designed to detect light intensities impinging on the image sensor 102 and represented by arrows.
  • the image sensor 102 is configured to output a luminous intensity information 104 that includes information about the light intensities detected in a partial area or an entire detection area of the image sensor 102.
  • An apparatus for estimating a screen-lattice effect of the image capture device is configured to receive a plurality of luminous intensity information 104 that is acquired by the image sensor 102 at different acquisition times and spent.
  • the device 106 is configured to determine an image characteristic of image information generated from the plurality of luminous intensity information 104.
  • FIG. 2 shows a block diagram of an apparatus for correcting a screen-lattice effect of an image capture device, according to an exemplary embodiment of the present invention.
  • the device has a correction device 212 which is designed to receive a luminous intensity information 104 detected and output by the image acquisition device and to receive suitable parameter values 108 for the correction of the effect of the screen screen.
  • the correction device 212 is further configured to correct the screen-mesh effect present in the luminous intensity information 104 using the parameter values 108 and to output a correspondingly corrected luminous intensity information 214.
  • FIG. 3 shows a flowchart of a method for estimating a screen-mesh effect of an image capture device, according to an exemplary embodiment of the present invention. For example, the method may be performed by the device 106 shown in FIG. By means of the method, parameter values 108 are generated which can be used, for example, by the correction device 212 shown in FIG. 2.
  • a plurality of luminous intensity information 104 are merged to determine a resulting luminous intensity information 323.
  • the resulting luminous intensity information is to be applied to parameter values 108 of a plurality of parameters in order to determine a luminous intensity information 327 applied to the parameters.
  • a smoothness 331 of the applied luminous intensity information 327 is determined.
  • a suitable mathematical or logical function can be used. The steps 325, 329 can be executed repeatedly with other parameter values 108 and the respectively resulting values of the smoothness
  • a set of optimal parameter values 108 may be found by a suitable algorithm, such as a minimization process.
  • optics In conventional 2D imaging methods, optics are used. This optics projects the luminous intensity information from the environment from a given solid angle onto a 2D sensor array. This can be represented as follows:
  • R 2 (1) l w , 2D can represent a luminous intensity information in the sensor plane.
  • the averaging can be done by a simple integration or summation or a concrete averaging in which, for example, the integral shown in equation (2) is still divided by the time T.
  • the assumption is based on the fact that the information lw, 2D varies over time in the real world. This may be because the camera and the optics are constantly moving, as is the case, for example, with vehicle front cameras, or due to other effects.
  • edges with maximum light intensity that can be recorded by the device change from image to image. It is easy to deduce how many samples need to be averaged. If there is an edge in an image and not in every other image, this edge should be used when averaging disappear. As a rule, a sensor can only measure light intensities of l M , 2D e [0-2 n - 1] DZ + . An edge of maximum light intensity should not contribute more than 1 after merging. Accordingly:
  • ri-sample-value indicates how many light intensities are merged.
  • a smoothness S of an information X can be expressed as follows:
  • I ⁇ I stands for any, but meaningful, norm.
  • S the smoother the picture.
  • the smoothness expresses a lack of high frequencies.
  • E Ll S (A w D ) L i + ⁇ a - ⁇ ) 2 + b 7 (16) min ⁇ ( E ⁇ or: min j ⁇ (17) a, ba, b
  • a plurality of luminous intensity information 104 are each subjected to a parameter value 108 in order to control a plurality of illuminated intensity values.
  • a value for a smoothness 447 of the respective applied luminous intensity information 443 is determined for each applied luminous intensity information 443.
  • the individual values of the smoothness 447 are combined to determine a resulting smoothness 451. Steps 441, 445, 449 may be with others
  • Parameter values 108 may be repeatedly executed and the respective resulting values of smoothness 451 may be compared with each other.
  • the parameter value 108 at which the value of the smoothness 451 is the lowest can be regarded as the optimum parameter value 108 and output.
  • the smoothness S (X) of a frame can be determined by the formula (6).
  • the merge e.g. the mean, of all particular smoothness.
  • the parameter set of the sensor model is constant in all smoothness calculations.
  • the mean, or merger, of the smoothness must be minimized to obtain noise reducing information. This minimization is again regulated by the parameters of the penalizer (alpha or alpha and beta).
  • the approaches of the invention are not limited to just correcting the DSNU or the PRNU. Instead, the approaches can be extended to any order of the device model.
  • the device model can be adapted in a way that the resulting equation system is smaller, eg only allowing one column FPN. Also, the way how the equation system can be solved is chosen freely, adapted to the required precision matching and the required processing complexity.
  • the approach of the invention is based on a basic physical assumption about the impression of the environment on the device.
  • An implementation may be in a program code of a computing device configured to exchange information with the camera. Also, an implementation can be done in hardware. Among other things, a hardware system also requires a memory for storing the averaged information.
  • the approaches according to the invention can be applied to all products for which clear and noise-free images are required.
  • One possible use is, for example, in a front camera of a vehicle.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Image Processing (AREA)
  • Picture Signal Circuits (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
  • Studio Devices (AREA)

Abstract

L'invention concerne un procédé d'évaluation de l'effet moustiquaire d'un dispositif d'enregistrement d'images comportant une pluralité de capteurs d'images fournissant une information relative à l'intensité lumineuse. Le procédé consiste à : déterminer un attribut d'image d'une information d'image qui est basé sur une pluralité d'informations relatives à l'intensité lumineuse (104) et une pluralité de paramètres (108), chaque paramètre de la pluralité de paramètres étant respectivement associé à un capteur d'images de la pluralité de capteurs d'images ; et déterminer (106) une pluralité de valeurs de paramètre pour la pluralité de paramètres (108) pour lesquelles l'attribut d'image est au moins proche d'un attribut d'image idéal, la pluralité de paramètres représentant l'effet moustiquaire dans un modèle du dispositif d'enregistrement d'images.
EP11769819.1A 2010-11-24 2011-09-28 Procédé et dispositif d'évaluation de l'effet moustiquaire d'un dispositif d'enregistrement d'images Withdrawn EP2643967A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102010061864A DE102010061864A1 (de) 2010-11-24 2010-11-24 Verfahren und Vorrichtung zur Abschätzung eines Fliegengittereffekts einer Bilderfassungseinrichtung
PCT/EP2011/066829 WO2012069238A1 (fr) 2010-11-24 2011-09-28 Procédé et dispositif d'évaluation de l'effet moustiquaire d'un dispositif d'enregistrement d'images

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EP2643967A1 true EP2643967A1 (fr) 2013-10-02

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US (1) US9154715B2 (fr)
EP (1) EP2643967A1 (fr)
JP (1) JP2013545410A (fr)
CN (1) CN103229497B (fr)
DE (1) DE102010061864A1 (fr)
WO (1) WO2012069238A1 (fr)

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WO2016102988A1 (fr) * 2014-12-24 2016-06-30 Datalogic Automation, Inc. Scanner multilignes

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JP2596316B2 (ja) * 1993-06-22 1997-04-02 日本電気株式会社 固体撮像カメラの固定パターンノイズ除去回路
US7362911B1 (en) * 2003-11-13 2008-04-22 Pixim, Inc. Removal of stationary noise pattern from digital images
US7294817B2 (en) * 2004-05-06 2007-11-13 Siemens Power Generation, Inc. System and methods for determining nonuniformity correction parameters in detector-array imaging
JP4487640B2 (ja) * 2004-06-01 2010-06-23 ソニー株式会社 撮像装置
US7684634B2 (en) * 2006-08-29 2010-03-23 Raytheon Company System and method for adaptive non-uniformity compensation for a focal plane array
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KR101442242B1 (ko) 2007-12-12 2014-09-29 삼성전자주식회사 불량 화소 및 잡음 제거 방법
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JP2009302850A (ja) * 2008-06-12 2009-12-24 Olympus Imaging Corp 固体撮像素子のノイズ除去装置、撮像装置、固体撮像素子のノイズ除去方法
KR20110048922A (ko) * 2009-11-03 2011-05-12 삼성전자주식회사 이미지 센서의 통합 노이즈 모델링 방법 및 이를 이용하는 노이즈 저감 방법

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JP2013545410A (ja) 2013-12-19
DE102010061864A1 (de) 2012-05-24
CN103229497B (zh) 2018-02-06
CN103229497A (zh) 2013-07-31
US9154715B2 (en) 2015-10-06
US20130308020A1 (en) 2013-11-21
WO2012069238A1 (fr) 2012-05-31

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