EP1470706A1 - Procede et dispositif de traitement d'images et systeme de vision de nuit pour vehicules - Google Patents

Procede et dispositif de traitement d'images et systeme de vision de nuit pour vehicules

Info

Publication number
EP1470706A1
EP1470706A1 EP02791599A EP02791599A EP1470706A1 EP 1470706 A1 EP1470706 A1 EP 1470706A1 EP 02791599 A EP02791599 A EP 02791599A EP 02791599 A EP02791599 A EP 02791599A EP 1470706 A1 EP1470706 A1 EP 1470706A1
Authority
EP
European Patent Office
Prior art keywords
image
images
camera
display
sensor
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Withdrawn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
EP02791599A
Other languages
German (de)
English (en)
Inventor
Ulrich Seger
Matthias Franz
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 EP1470706A1 publication Critical patent/EP1470706A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/63—Control of cameras or camera modules by using electronic viewfinders
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/14—Picture signal circuitry for video frequency region
    • H04N5/20—Circuitry for controlling amplitude response

Definitions

  • the invention relates to a method and a device for image processing as well as a night vision system for motor vehicles.
  • the optical sensor camera, sensor array
  • the image sharpening the quality did of near infrared 'captured images greatly improved, so that use as a night vision system is made possible for motor vehicles.
  • the image is sharpened by an image processing module that sharpens the blurred images that result from reflection of the incident infrared light on the rear wall of the optical sensor as a result of the infrared transparency of the silicon of the sensor chip.
  • image processing module that sharpens the blurred images that result from reflection of the incident infrared light on the rear wall of the optical sensor as a result of the infrared transparency of the silicon of the sensor chip.
  • an adaptive mapping function preferably a non-linear characteristic curve
  • contrast enhancement which maps the gray values present in the image into the value range of the gray values of the display.
  • a sensor with a higher number of gray values can advantageously be used in conjunction with a display which can only display fewer gray values.
  • the resulting, contrast-enhanced image shows the viewer all the essential details, since frequent gray value areas are displayed with high resolution, less frequent gray value areas with lower resolution.
  • image sharpening and contrast enhancement are advantageously used together or a procedure alone.
  • FIG. 1 shows an overview image with a flow chart for an image processing system, in particular a night vision system for motor vehicles, in which the above-mentioned image sharpening and contrast enhancement modules are used.
  • FIGS. 2 and 3 two preferred exemplary embodiments are explained which are used to form the imaging function for contrast enhancement.
  • FIG. 4 shows, using a flow chart, an implementation example of the procedure described as a computer program. Description of exemplary embodiments
  • FIG. 1 shows the structure of an image processing system with an optical sensor 10, in particular a standard camera sensitive in the near infrared, which outputs raw images to a processing unit 12.
  • the processing unit 12 which, depending on the exemplary embodiment, consists of separate signal processing units or a digital processor, has an image sharpening module 14 and a contrast enhancement module 16.
  • the image sharpening module 14 converts the raw images supplied by the camera 10 into sharpened images that are output to the contrast enhancement module.
  • the contrast enhancement module 16 consists of an imaging function 18, preferably a non-linear characteristic curve, which converts the sharpened images supplied by the image sharpening module 14 into high-contrast images adapted to the display 20 used.
  • an imaging function calculation module 22 is part of the contrast enhancement module.
  • the contrast enhancement module 16 then outputs the sharpened images weighted by means of the characteristic 18 as contrast-enhanced images to the display 20 for display.
  • the camera 10 is a standard camera that is used in many applications and is sensitive in the visible range and in the near infrared. The sensitivity in the near infrared is achieved, for example, by dispensing with filter measures, which are sometimes provided with such a standard camera.
  • the display 20 is a display means which can display a smaller number of gray values than the camera. A preferred example is an LCD Display on which the images captured by the camera 10, in particular night vision images of the motor vehicle environment, are displayed so that they are visible to a driver.
  • the image enhancement module and contrast enhancement module are used together or separately (i.e. only one of the modules).
  • the sequence of the procedure, first image sharpening, then contrast enhancement is advisable when using both modules for image processing.
  • the system shown in FIG. 1 represents a night vision system for motor vehicle applications in which the standard camera takes a picture in the near infrared in combination with suitable lighting of the road area in front of the vehicle.
  • the meaning can e.g. come from a vehicle headlight or an auxiliary headlight that has infrared light components.
  • the recorded night vision images are then shown to the driver using the image processing shown on the display.
  • a camera is used which can display up to 4096 gray values, while the display used can typically only display 64 gray values.
  • the image sharpening module 14 works with the aid of an image sharpening method in which the edges in the image that appear washed out due to the blurred image are reinforced.
  • so-called inverse filtering is used as the method.
  • the blurring of the image in the near infrared is described by a so-called point washing function or by its Fourier transform, the modulation transfer function. In the preferred exemplary embodiment, this is carried out for the camera type by a standard method in the case of a vehicle headlight. matched infrared lighting.
  • This modulation transfer function is inverted and transformed back into the spatial area, so that a filter mask is obtained which ideally exactly compensates for the blurring caused by reflection.
  • This filter mask has a high-pass characteristic in relation to the spatial frequencies, ie lower spatial frequencies are attenuated more than higher spatial frequencies.
  • An exact compensation of the reflection-related unsharpness can generally not be achieved in practice through quantization and / or saturation effects, so that some details are lost. However, it has been shown that this is irrelevant when using a display with a lower resolution, since the loss of detail is imperceptible to the viewer. The method therefore removes the existing blur in the image to a sufficient extent.
  • the image sharpening module thus results in an increased contrast of the image in the region of the short spatial wavelengths when recording a test pattern, for example comparable to the known Siemens star, i.e. of the higher frequencies.
  • This effect becomes clearer if a sinusoidal intensity curve is specified for the test pattern instead of the rectangular one, since the influence of higher harmonics is thus eliminated, and the amplitude of the intensity fluctuation is still not maximum.
  • a first method for contrast enhancement is shown on the basis of the diagram in FIG.
  • the gray values of the GWD display are shown above the gray values of the GWK camera.
  • the starting point is the histogram 100 of the gray values of the camera image, which is also shown in FIG. 2.
  • the frequency of the individual gray values in the analyzed image is plotted in this histogram.
  • a gray value range of the camera image is then derived from the histogram 100, which covers a predetermined percentage of the gray values of the camera image.
  • the limits of this gray value range are set such that a predetermined percentage of the low gray values and a certain percentage of the highest gray values of the camera image (for example 5% each) do not fall within the intended section.
  • the position of the section boundaries is determined for each camera image (minimum and maximum gray value).
  • a characteristic curve 102 is then derived on the basis of the calculated section boundaries, which is piece-wise linear in the embodiment shown and maps m gray values of the camera image to n gray values of the display (usually m> n).
  • the characteristic curve or the mapping function consists of three sections, in the first section all camera gray values are shown on the lowest gray value of the display, in the middle section m-gray values of the camera image are shown on n-gray values of the display, in the last section all camera gray values are shown on the highest display gray value.
  • the linear part of the function 102 converts gray areas of the camera to a gray value of the display.
  • the function therefore carries out a "clipping" operation and compresses or spreads the middle gray value range of the camera image to the display gray values.
  • FIGS. 3a and 3b A second method is illustrated using the illustration in FIGS. 3a and 3b.
  • This process is called a histo- referred to as grammatical comparison.
  • the cumulative distribution function is formed from the histogram 100 by adding up the histogram values one after the other (integrated). This is shown in FIG. 3a, in which the frequency of the camera gray values is plotted against the possible camera gray values.
  • a non-linear characteristic or mapping function is then determined according to FIG. 3b by adding up the frequency values, which is shown in FIG. 3b.
  • This characteristic curve is the basis for the conversion of the gray value range of the camera GWK to the gray value GWD of the display.
  • the mapping curve shown assigns each gray value area of the camera image the optimal resolution in the gray value area of the display. Since the number of gray values on the display is less than that on the camera image, the imaging curve must be undersampled with the number of gray values available on the display. In this way, only one gray value of the display is assigned to certain gray value areas of the camera. More frequent camera gray values (see area of greater slope of the characteristic curve) are assigned a larger number of display gray values (higher resolution), and fewer camera gray values (flatter characteristic curve area) are assigned a smaller number of display gray values.
  • the characteristic curve or mapping function is recalculated for each image or for every nth image using one of the methods mentioned.
  • the mapping function is preferably low pass filtered to suppress noise in the histogram.
  • the image processing is carried out in a computer which converts the raw images supplied by the camera into high-contrast, sharpened images for display purposes.
  • the procedure described above is implemented as a computer program. An example of such a computer program is outlined in the flow chart of FIG.
  • the raw image supplied by the camera is read in according to step 200.
  • the image is filtered using the specified filter mask. This represents the sharpened image.
  • the histogram is derived from this image in accordance with step 206 and the imaging function is generated in step 208 in accordance with one of the methods described above.
  • the sharpened image is evaluated with the determined imaging function, i.e. Gray value areas of the image are converted into a display gray value in accordance with the imaging function and then output in step 212 to the display for display.
  • the program outlined using the flow diagram is run through for each raw image.
  • the mapping function is only determined for a predetermined number of images (e.g. every tenth).
  • the preferred application of the procedure shown is a night vision system for motor vehicles.
  • the procedure shown can also be used outside of this application with other night vision systems or with image processing systems in the near infrared or in the visible range, in which unsharp images arise or images of high resolution are displayed by means of displays of lower resolution.
  • the procedure described can also be used in connection with color images.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Image Processing (AREA)
  • Studio Devices (AREA)

Abstract

Procédé et dispositif de traitement d'images et système de vision de nuit pour véhicules. Selon ledit procédé, une caméra standard sensible dans la plage de l'infrarouge proche produit des images qui sont affichées sur un moyen d'affichage présentant une résolution inférieure à celle de la caméra. La présente invention concerne en outre un traitement d'images qui améliore les images brutes du capteur à l'aide d'un procédé d'intensification de la netteté des images et / ou d'amplification des contrastes, ce qui permet l'affichage sur un moyen d'affichage en vue de l'observation par un observateur.
EP02791599A 2002-01-22 2002-11-25 Procede et dispositif de traitement d'images et systeme de vision de nuit pour vehicules Withdrawn EP1470706A1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
DE10202163 2002-01-22
DE10202163A DE10202163A1 (de) 2002-01-22 2002-01-22 Verfahren und Vorrichtung zur Bildverarbeitung sowie Nachtsichtsystem für Kraftfahrzeuge
PCT/DE2002/004318 WO2003063468A1 (fr) 2002-01-22 2002-11-25 Procede et dispositif de traitement d'images et systeme de vision de nuit pour vehicules

Publications (1)

Publication Number Publication Date
EP1470706A1 true EP1470706A1 (fr) 2004-10-27

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EP02791599A Withdrawn EP1470706A1 (fr) 2002-01-22 2002-11-25 Procede et dispositif de traitement d'images et systeme de vision de nuit pour vehicules

Country Status (4)

Country Link
US (2) US7474798B2 (fr)
EP (1) EP1470706A1 (fr)
DE (1) DE10202163A1 (fr)
WO (1) WO2003063468A1 (fr)

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Also Published As

Publication number Publication date
US7693344B2 (en) 2010-04-06
US20090141128A1 (en) 2009-06-04
US7474798B2 (en) 2009-01-06
US20040136605A1 (en) 2004-07-15
WO2003063468A1 (fr) 2003-07-31
DE10202163A1 (de) 2003-07-31

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