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WO2012007061A1 - Procédé pour la détection et la correction d'une aberration chromatique latérale - Google Patents

Procédé pour la détection et la correction d'une aberration chromatique latérale Download PDF

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Publication number
WO2012007061A1
WO2012007061A1 PCT/EP2010/060367 EP2010060367W WO2012007061A1 WO 2012007061 A1 WO2012007061 A1 WO 2012007061A1 EP 2010060367 W EP2010060367 W EP 2010060367W WO 2012007061 A1 WO2012007061 A1 WO 2012007061A1
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Prior art keywords
color plane
edge
shift
edges
determining
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PCT/EP2010/060367
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English (en)
Inventor
Johan Schirris
Sasa Cvetkovic
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Robert Bosch Gmbh
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Priority to DE112010005744.3T priority Critical patent/DE112010005744B4/de
Priority to PCT/EP2010/060367 priority patent/WO2012007061A1/fr
Publication of WO2012007061A1 publication Critical patent/WO2012007061A1/fr

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    • 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
    • H04N25/61—Noise processing, e.g. detecting, correcting, reducing or removing noise the noise originating only from the lens unit, e.g. flare, shading, vignetting or "cos4"
    • H04N25/611—Correction of chromatic aberration
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/10—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths
    • H04N23/12—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths with one sensor only
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/80—Camera processing pipelines; Components thereof
    • H04N23/84—Camera processing pipelines; Components thereof for processing colour signals
    • H04N23/843—Demosaicing, e.g. interpolating colour pixel values
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
    • H04N25/10—Circuitry of solid-state image sensors [SSIS]; Control thereof for transforming different wavelengths into image signals
    • H04N25/11—Arrangement of colour filter arrays [CFA]; Filter mosaics
    • H04N25/13—Arrangement of colour filter arrays [CFA]; Filter mosaics characterised by the spectral characteristics of the filter elements
    • H04N25/134—Arrangement of colour filter arrays [CFA]; Filter mosaics characterised by the spectral characteristics of the filter elements based on three different wavelength filter elements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N2209/00—Details of colour television systems
    • H04N2209/04—Picture signal generators
    • H04N2209/041—Picture signal generators using solid-state devices
    • H04N2209/042—Picture signal generators using solid-state devices having a single pick-up sensor
    • H04N2209/045—Picture signal generators using solid-state devices having a single pick-up sensor using mosaic colour filter

Definitions

  • the current invention relates to a method for lateral chromatic aberration detection and correction, to a corresponding optical image processing device and to a corresponding computer program product.
  • achromatism or chromatic distortion is a defect of optical lenses in connection with focusing all colors to the same convergence point at a certain distance of the lens (axial or longitudinal chromatic aberration) and/or at a certain location in the focal plane (transverse or lateral chromatic aberration). Both types of aberration are caused by different refractive indices of lenses for different wavelengths (dispersion).
  • Chromatic aberration is evident from images in form of color fringes along boundaries separating dark and bright areas (edges).
  • the visual effects of longitudinal and lateral chromatic aberration are different in that longitudinal chromatic aberration causes fringes in all places in the scene, whereas lateral chromatic aberration affects objects stronger if they are farther from the center.
  • Fringes caused by lateral chromatic aberration in contrast to those due to longitudinal chromatic aberration, are typically absent in the image center (typically coinciding with the lens center) and progressively increase toward the image corners.
  • the performance of color separation, involved in digital image processing, is strongly affected by lateral chromatic aberration and the resulting misalignment of the different color planes.
  • Chromatic aberration can be reduced or eliminated by using achromatic and apochromatic lenses comprising glasses with different dispersion.
  • achromatic and apochromatic lenses comprising glasses with different dispersion.
  • Such lenses are heavy and expensive.
  • a reduction of chromatic aberration by stopping down lenses is, especially in case of lateral chromatic aberration, not always practicable, desired or effective.
  • LCA Methods for reducing lateral chromatic aberration, hereinafter referred to as LCA, are known from US 2008/0284869 A1 , US 7,221 ,793 B2, US 2008/0291447 A1 , US 6,747,702 B1 , US 7,227,574 B2, US 2009/0052769 A1 , US 7,577,292 B2, US 2007/0242897 A1 , US 7,346,210 B2, US 7,142,238 B1 , JP 2002 320237 A, US 7,425,988 B2, JP 2000 299874 A, US 2008/0007630 A1 , US 7,466,495 B2,
  • the current invention includes providing an LCA model to estimate LCA magnification or shift parameters caR and caB, corresponding to a chromatic aberration shift of the red (in the following referred to as "R") and blue (“B") color plane or component with respect to the green ("G”) color plane or component with sub-pixel accuracy, from an arbitrary input image.
  • the method does thus not require use of a reference image known in advance.
  • LCA correction is performed with the LCA parameters thus determined to remove LCA deformations from the image.
  • Figure 1 schematically illustrates a Bayer grid according to the prior art.
  • Figure 2 illustrates an horizontal edge of a color signal including an LCA shift.
  • Figure 3 illustrates a radial LCA shift of a pixel of a R color plane.
  • Figure 4 illustrates a radial LCA color shift of a R color plane in an image.
  • Figure 5 illustrates a radial LCA color shift of a B color plane in an image.
  • Figure 6 illustrates a method for LCA detection and correction according to a preferred embodiment of the invention.
  • Figure 7 illustrates a method for LCA detection according to a preferred
  • Figure 8 illustrates a method of detecting position of an edge of a color signal according to a preferred embodiment of the invention.
  • Figure 9 illustrates a method of calculating a correction factor according to a preferred embodiment of the invention.
  • Figure 10 illustrates details of calculating a correction factor according to a
  • Figure 1 1 illustrates the restriction of an LCA determination to an image area according to a preferred embodiment of the invention.
  • Figure 12 illustrates a method of calculating an LCA shift according to a preferred embodiment of the invention.
  • Figure 13 illustrates a method of calculating an edge position with sub-pixel accuracy according to a preferred embodiment of the invention.
  • Figure 14 illustrates a bilinear interpolation on a pixel grid according to a
  • Figure 15 illustrates a method of determining a shift vector according to a
  • FIG. 1 an image sensor 100 in form of a Bayer grid sensor as frequently used in digital image recording and/or processing devices is shown. It should be noted that, even if the current application is exemplified with a Bayer grid sensor, the methods described herein are equally useful with other types of image sensors including grids wherein different color pixels are recorded at different locations.
  • the R, G (in form of G1 and G2) and B pixels are placed in a so-called Bayer pattern.
  • a color separator is required to interpolate (find an estimate) of R, G and B values at the current pixel on a position (x, y) by using existing samples in the neighborhood of the current location.
  • This image "reconstruction" to a full resolution can only be performed satisfactorily if all three image planes are well aligned, i.e. when no shift exists between them.
  • lateral chromatic aberration misaligns color planes and thus reduces the performance of the color separator.
  • LCA An example of LCA is presented in Figure 2, where a horizontal edge 200 in an image including a horizontal (x) color shift of the R and B color planes (dRx, dBx) is shown. A similar effect can also be noticed in the vertical (dRy, dBy) or any other direction.
  • r shall represent a radial distance of the current pixel from the center of the sensor (typically coincident with the center of the lens) and caR and caB are parameters of the LCA model for the R and B color planes respectively. In the following description, these two parameters will also, commonly, be referred to as caRB.
  • LCA misalignment can be modeled by a 3 rd order polynomial: caRB 3 * r 3 + caRB 2 * r 2 + caRB- ⁇ * r 1 + caRB 0
  • the function f(r) describes a shift of the R and B pixels with respect to the (reference) G pixel and is depicted as a shift vector (dRx, dRy) in Figure 3.
  • the "real" values of the R or B pixels on any position (x, y) are not the ones that are measured on that position but are displaced to a new location given by the shift vector. This new position is usually not located on the existing pixel grid, so the real pixel's value has to be estimated from its neighbors k, I, m and n.
  • LCA color shifts in an image with 25 * 25 pixels are exemplified in Figure 4 (for the R plane) and Figure 5 (for the B plane). It can be noticed that shifts are approximately zero in the image center and that they increase in their absolute value towards the image edge.
  • the steps of the zero-crossing based detection method according to an embodiment of the current invention are schematically shown in Figure 6.
  • the current invention includes detecting 10 LCA directly from the image and estimating or determining 20 the LCA magnification parameters caR and caB.
  • a LCA parameter calculation method is employed which directly estimates the LCA shift from the image data, based on the detection of zero-crossings.
  • LCA correction 40 is performed via re-sampling the R and B color planes.
  • Input to the methods of the invention are R, G and B color planes originating, e.g., from a Bayer pattern sensor as in Figure 1 .
  • the LCA shift between the G and R (or B) color channels is measured by matching corresponding edges in these color channels and by calculating their distances (dRx, dRy, dBx and dBy, cf. Figure 2) with sub-pixel accuracy.
  • an edge detection for all three color channels needs to be performed and the edge distances between the R and the G as well as the B and the G color channel need to be determined.
  • the color planes originating e.g., from a Bayer grid, the values of the R, G and B color pixels on each location are not directly available.
  • LCA_R and LCA_B of relevant data for all edge pixels which are taken for the LCA detection are generated. For instance, the first two columns of such lists are the line and the pixel number of the edge pixel and the third column is a detected shift (which can be positive, negative or zero).
  • the overall steps of LCA detection are set forth below and illustrated in Figure 7.
  • these steps include recalculating 1 the G color samples on the R and B Bayer grid position, low-pass filtering 2 the color planes as preparation for the edge detection, edge detection 3 using a Laplacian operator and a set of conditions, calculating 4 a correction factor for an edge shift, a check 5 whether the detected edge pixels satisfy a set of conditions, calculating 6 a LCA shift between color planes with sub-pixel accuracy and generating 7 a list of data for detected edge pixels, marking their position and LCA shift.
  • G color samples can be recalculated 1 (see Figure 7) to correspond to the R and B pixel positions. These new color planes will be referred to as GonR and GonB.
  • a second option is to correct for the edge shift that is equal to 0.5 pixels during calculation of the edge shift. For instance, for calculation of the horizontal edge shift between the G and B color planes, an edge detection on the G2 (cf. Figure 1 ) pixels and, in the vertical direction, on the G1 pixels may be performed.
  • a low-pass filtering 2 (cf. Figure 7) of the R, B, GonR and/or GonB color planes is performed as preparation for the edge detection.
  • Edge positions should be detected with sub-pixel accuracy on all color planes. Edges can, in the context of the current application, be detected, e.g., by means of a Sobel or Canny or any other edge detector. Many of the known methods, however, do not produce one pixel wide edges. Therefore, consecutive edge tinning must be applied. However, with 1 st order high-pass filters it may be difficult to find a sub-pixel accuracy position of the edge. Therefore, preferably edge detection is performed including filtering 3 to improve the noise robustness (cf. Figure 7) using a second order high-pass filter (e.g., a Laplacian filter [-1 2 -1 ]) which has zero output on the position of an edge.
  • a second order high-pass filter e.g., a Laplacian filter [-1 2 -1 ]
  • a low-pass filter can be applied in the direction opposite to the main filtering direction. Instead of applying a LOG filter with a pre-filtering operation in the opposite direction from the edge detection operation, also a 2D low-pass filtering and then a simple Laplacian filtering operation may be performed.
  • this operation is advantageously performed for robust and accurate edge detection, however, it is equally beneficial for the line-angle detection operation (described below) where a 1 st order high-pass filter is used.
  • a 2D low-pass filter that can be advantageously used for filtering all color channels (R, GonR, GonB and B) is given by
  • candidate edges for further processing are to be determined.
  • a Laplacian filter [-1 2 -1 ] will be applied on all color planes, separately in the horizontal and vertical direction.
  • Such edges advantageously include isolated edges with sufficient magnitude (dynamic range).
  • the high-pass output of the Laplacian filter is shown in form as a signal hp.
  • the real position E of the edge of the signal C is usually not positioned on one of the pixels px-1 , px, px+1 and px+2, and can be determined by the zero crossing.
  • a reference point P of the edge pixel found with a pixel accuracy on the position px is used.
  • the value ⁇ indicates the sub-pixel position of the zero crossing (i.e., the distance between the real edge position E and the position P of the detected edge pixel). While crossing zero, the sign 800 of the hp signal changes its value, for example, from - to +.
  • TH ⁇ (C(px) - C(px+1 )) ⁇ > TH.
  • two samples on the left of the edge zero-crossing and two samples on the right of the edge zero-crossing should have the same sign, while hp changes sign between the two middle samples.
  • the edge height needs to be larger than a threshold TH.
  • a check on the edge size is performed on the image itself, not on the high-pass signal.
  • TH can be a constant, but may equally depend from the global contrast in the image or even on a local contrast.
  • the step of applying the condition set on the detected edges is performed separately in the horizontal and vertical direction for all four color planes.
  • Detection in the diagonal direction is also advantageous but more complex.
  • a correction factor (CF) is calculated.
  • CF correction factor
  • Figure 9 a detected edge on the GonR (see above) color channel and its corresponding edge detected in the R color channel is shown in a quadrant of an image with an image center at (Xc, Yc).
  • a distance between R and GonR (the G color plane at the R position) is to be calculated (see also Figure 2).
  • the distance calculation should (cf. Figures 4 and 5) most advantageously be performed in the radial direction 900, resulting in a distance d.
  • this operation is challenging to be performed correctly.
  • the distance d between the lines is measured in the horizontal dx or vertical dy direction, depending on the edge direction.
  • the radial distance of two edges is not equal to their horizontal distance (distance dx between A and C in Figure 9) or vertical distance (distance dy between A and D in Figure 9).
  • CF correction factor
  • CF depends on two angles, namely the pixel angle ⁇ and an edge angle a. Angles are defined counter-clockwise, starting from the positive direction of the x-axis, where the x-axis and the y-axis pass trough the image center (Xc, Yc).
  • the Angle ⁇ (in Figure 9) represents an angle of the current pixel with respect to the image center. It is in the range from 0 to 2 ⁇ .
  • a is an angle of the considered edge trough that pixel and is in the the range from 0 to ⁇ .
  • edge pixel denoted A in Figure 9 will be shifted in the R (or B) color plane to a position B in the radial direction (in the following, a shift outwards will be assumed and assigned a positive sign).
  • the shift distance is d.
  • the shift is measured in the horizontal and in the vertical direction.
  • a search is performed in the horizontal direction around pixel A and a pixel D will be found in a distance dx.
  • a pixel C will be found in a distance dy.
  • edge pixels C and D originate from pixels A-i and A 2 from the edge line in the G color channel, respectively, and not from the considered edge pixel A. As long as pixels A, A-i and A 2 are collinear, this does not represent a problem, but if they do not lie not on the same line, the pixels C and D are falsely determined, and, thus, this edge pixel has to be ignored.
  • Figure 10 includes further details when compared with Figure 9.
  • the left part of Figure 10 largely corresponds to Figure 9 whereas the right part of Figure 10 shows an enlarged portion of the shaded region in the left part of Figure 10.
  • CFx can be derived as follows (cf. Figure 10, right part):
  • CFy can be derived as follows (cf. Figure 10, right part):
  • CF Since only the modulo and not the sign of dx and dy should be changed by CF, CF is defined advantageously with an absolute value so that it is valid for all four quadrants of the angle ⁇ and all angles a.
  • CFx I sin(a)/sin(a - ⁇ )
  • , CFy
  • a check is performed whether detected edge pixels satisfy a set of conditions.
  • the detected edge should be an isolated one to ensure that we the same edge is matched in different color planes. Hence, for example, around the current GonR edge pixel there should to be no other detected edge in the GonR color plane. Otherwise, a detected R edge could be matched with the wrong edge in GonR.
  • This search should be advantageously performed in a consistent manner and isolated edge pixels should be ignored. Therefore, one has to check for a set of conditions (which will be explained in the following regarding the example of the R color plane but are equally valid for the B color plane):
  • edge angle a ( Figure 9) should be between ⁇ /4 and 3 ⁇ /4; for the vertical direction, the angle a should be between [0 ... ⁇ /4] or [3 ⁇ /4 ...
  • the edges in the R and GonR as well as in the B and GonB color planes should have a similar angle a to insure the same edge is matched, the correction factor CF is between TH-i (for instance 0.5) and TH 2 (for instance 2); the larger or the smaller the CF, the more error is introduced to the LCA measurement, so such edge pixels will be skipped; the LCA shift according to its simple model is linearly dependent on the radial distance of the pixel from the image center:
  • this shift is very small and can not be estimated well, since mainly noise will be measured.
  • r 0 can also set as depending on the maximum expected LCA shift and to a value where, for instance, the absolute value of the LCA color shift equals half a pixel.
  • a rectangle can be used (small rectangle in Figure 1 1 ). In this case, the acceptance condition is that the current pixel coordinates (x, y) satisfy the criteria
  • a solution for this problem advantageously includes to exclude all measurements in the neighborhood of very bright pixels. Likewise, all pixels in (- ⁇ , ⁇ ) neighborhood of the bright pixel in both horizontal and vertical direction may be excluded.
  • the search area is a parameter that depends on a lens, a radial distance from the image center and an edge angle.
  • n an optimal value of n can be determined in a loop, starting with a larger n and, depending on the results (sufficient amount of detected edges and a reliable measurement), leaving it as it is or starting to reduce it. If a real maximum shift of LCA is larger than the value of n used, serious errors are introduced in the measurement since pixels could be matched which belong to different edges (objects). If blue and red shift are always of the opposite sign, as well as if the direction of the shift is known (for instance, the shift of red always is directed out from the center and blue shift towards the center), the algorithm may be improved since one knows in advance in which direction to search for
  • step 6 an LCA shift between R, GonR, GonB and B color planes is calculated.
  • Four pixels which are used for the detection of the edge position are /?p(px-1 ), hp(px), hp(px+ ) and hp(px+2) are illustrated in Figure 12. If an edge on a GonR or GonB color channel is detected on a position px, the real sub-pixel edge position is at a position of px + ⁇ - ⁇ , ⁇ - ⁇ ⁇ 1 , on a corresponding grid.
  • ⁇ - ⁇ is a sub-pixel distance from the second pixel to the zero crossing in the GonR (or GonB) color plane.
  • Figure 8 two middle pixel values hp(px) and hp ⁇ px+ ) have an opposite sign.
  • dx and dy are depicted in
  • the LCA shift vector is given as CF * ( ⁇ - ⁇ - ⁇ + ⁇ 2 ) on a R (or B) color grid (see Figure 12).
  • CF * ( ⁇ - ⁇ - ⁇ + ⁇ 2 ) on a R (or B) color grid (see Figure 12).
  • this distance is two times larger.
  • a candidate edge pixel was recorded with its coordinates (line and pixel number) and a value of the lateral chromatic aberration (shift between color planes) calculated in units of pixels on the R or B color plane.
  • a sub-pixel accuracy edge position depicted with ⁇ - ⁇ ( ⁇ ⁇ 2 ) ⁇ Figure 12 is calculated.
  • a better (and more complex) approximation of a real edge position can be achieved using, for instance, a cubic interpolation.
  • a position x is to be found at which interpolation value y(x) yields a value equal to zero (zero- crossing point).
  • an analytical solution is complex to be found directly, so advantageously values
  • a step of 0.1 can be taken, which represents a target sub-pixel accuracy of 0.1.
  • , is a good approximation of ⁇ - ⁇ for the GonR edge and ⁇ 2 for the R edge (from Figure 12).
  • LCA can be seen as different magnification of the R and B color planes.
  • the parameter caR represents a magnification of the R color plane and caB of the B color plane. caR and caB are used afterwards to construct shift vectors of each R (or B) pixel with respect to the G pixel.
  • Real magnification is equal to 1 + caRB (caRB representing both caR and caB LCA parameters).
  • shift d is 1 pixel on a R (or B) color plane, which is equal to 2 pixels on the full Bayer grid.
  • the parameter caRB can be estimated as d/r.
  • a value of caRB with a least mean-square error is estimated as:
  • caR which is a LCA parameter for R color plane
  • ⁇ can represent a standard deviation of the measured data and caRB is estimated in the previous step. However, measured standard deviation can be very large, so ⁇ may be limited by the mean value itself (caRB).
  • R/B data are resampled to correct for the lateral chromatic aberration shift.
  • Lateral chromatic aberration and its 3 rd or 1 st order shift functions f(r) have been discussed before.
  • the function f(r) describes a shift of the R (B) pixels with respect to the G pixel which is taken as a reference.
  • the shift is depicted as a shift vector in Figure 14.
  • the real values of the R or B pixels at any position (x, y) are not the ones that are measured on that position but are displaced to a new location given by the shift vector. This new position is, for most of the time, not located on the existing pixel grid, so those values have to be estimated from their neighbors.
  • bilinear interpolation in case of the bilinear interpolation, four pixels k, I, m and n around the real pixel position are taken by observing the shift vector.
  • the integer part of the shift vector determines which four pixels to take, and fractional part determines weights w x and w y for the interpolation.
  • bilinear re-sampling is performed due to its simplicity. Bicubic or any other re-sampling is also possible, however somewhat more expensive since calculations with more pixels (for instance 16) and likewise more line memories (3 or more instead of 1 ) are needed.
  • a LCA shift vector is calculated for each R and B pixel under examination.
  • the vector depends on the chromatic aberration parameters caR (caB) and radial distance r of the pixel with respect to the optical image center (Xc, Yc).
  • x and y are pixel coordinates starting at the top left image corner and increasing to the right and towards down, respectively (see Figure 15).
  • the shift vector can be represented in terms of the x and y position of the current pixel in the image avoiding calculating its radial distance r.
  • caR 0 and caB 0 are present to accommodate a possibility that a value of LCA is not equal to zero in the optical center of the image.
  • an offset has to be introduced:
  • Yc Total number of pixels (vertical) 1 2 + offset (vertical).
  • all values of R and B pixels are recalculated to correct for effect of LCA, and good color separation (finding a R, G and B pixel value at each location) can be performed afterwards.
  • this technique is also applicable when R, G and B pixel values already exist on all grid positions, but LCA deteriorates the image quality.

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Abstract

La présente invention se rapporte à un procédé pour la détection (10) et la correction (40) d'un décalage avec aberration chromatique latérale d'un plan de couleur rouge (R) et/ou d'un plan de couleur bleue (B) par rapport à un plan de couleur verte (G) dans des données d'image numériques. Le procédé selon l'invention consiste : à déterminer (3), pour au moins une région des données d'image numériques, un premier bord dans le plan de couleur rouge (R) et/ou le plan de couleur bleue (B) ; à déterminer (3) un second bord dans le plan de couleur verte (G) ou dans un plan de couleur (GonR, GonB) dérivé (1) du plan de couleur verte (G), le ou les seconds bords correspondant au(x) premier(s) bord(s) ; à déterminer (6) un décalage entre les premier(s) et second(s) bords ; et à corriger (40) le décalage avec aberration chromatique latérale en décalant le plan de couleur rouge (R) et/ou le plan de couleur bleue (B) de la région d'image.
PCT/EP2010/060367 2010-07-16 2010-07-16 Procédé pour la détection et la correction d'une aberration chromatique latérale WO2012007061A1 (fr)

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DE112010005744.3T DE112010005744B4 (de) 2010-07-16 2010-07-16 Verfahren für die Detektion und Korrektur einer lateralen chromatischen Aberration
PCT/EP2010/060367 WO2012007061A1 (fr) 2010-07-16 2010-07-16 Procédé pour la détection et la correction d'une aberration chromatique latérale

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CN113850738A (zh) * 2021-09-24 2021-12-28 上海富瀚微电子股份有限公司 图像紫边的校正装置及方法
CN114283077A (zh) * 2021-12-08 2022-04-05 凌云光技术股份有限公司 一种校正图像横向色差的方法
WO2024013405A1 (fr) * 2022-07-13 2024-01-18 Jj Gestión Integral S.L. Procédure de réduction de l'aberration chromatique latérale dans des écrans par visualisation à travers des lentilles et dispositif pour une telle procédure

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