A method for producing a high-resolution color holographic structure

Abstract

The application provides a high-resolution color holographic structure preparation method, and specifically comprises the following steps: S1, obtaining three-dimensional information of an object; S2, extracting and separating three primary color channels of an image array; S3, setting a convolution kernel, and respectively performing convolution on each color channel component of each image; S4, respectively performing pooling on the extracted data; S5, respectively extracting the numerical values of each same coordinate position in the set to form a new data set by using an addressing method; S6, designing three surface relief gratings with different periods according to a grating equation; S7, respectively performing point multiplication on the three primary color matrices and the grating data; S8, re-fusing the encoded three color channel data; and S9, manufacturing the data file into a moldable subwavelength level surface relief structure by a micro-nano processing method. The application converts surface relief micro-nano structures by separating, optimizing, fusing and encoding the multi-angle sampling image array data, and the imaging space has strong stereoscopic sense, bright color, strong detail performance and great imitation difficulty.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical naked-eye 3D display technology, and particularly relates to a high-resolution color holographic structure preparation method. BACKGROUND

[0002] In the late 1970s, scientists discovered that holographic pictures have surface structures including three-dimensional information, and this structure can be transferred to high-density photosensitive films and other materials. Until 1980, American scientists began to use embossed holograms to transfer holographic surface structures to polyester films, thus successfully printing the world's first embossed holographic picture. This laser holographic picture is also known as a rainbow holographic picture, which is produced by laser plate making to produce a five-color diffraction effect and make the picture have two-dimensional and three-dimensional spatial stereoscopic effect. Hidden images and information will be revealed under certain light. When light is incident from another angle, new images will appear. This embossed holographic picture can be mass-produced and quickly copied like printing, with low cost and can be combined with various printed products.

[0003] As a crystallization of high-tech, laser holographic anti-counterfeiting products have been widely concerned in recent years as a modern laser application technology. As described above, it has many advantages: laser holographic anti-counterfeiting labels have unique anti-counterfeiting functions compared to general printed trademarks, clear images, colorful colors, three-dimensional effect, one-time use, and can increase product aesthetics, and are favored. There are also reports on laser holographic anti-counterfeiting products in the prior art. For example, Chinese patent application CN113126465A discloses a three-primary-color color holographic metasurface based on dual-channel polarization multiplexing and a design method thereof. The color holographic metasurface uses two orthogonal circular polarization channels to process color images R, G, and B three primary color components, and performs corresponding dispersion compensation for each component wavelength, solving the color holographic Figure Three cross-talk problem of primary color components, and combining the advantages of angle multiplexing and polarization multiplexing. On this basis, by phase control of each periodic nanometer unit constituting the metasurface, a phase-type computer hologram is realized, which can present a color hologram in the far field under normal incidence.

[0004] But the current laser holographic anti-counterfeiting technology also has obvious shortcomings. The current laser holographic anti-counterfeiting technology mainly directly replaces the gray value in the object sampling image with a corresponding different direction angle and different period grating or texture, and then nests and combines the replaced dot matrix grating to form a hologram. Therefore, the more visual images collected, the larger the surface relief grating structure. Therefore, the available ironing holographic three-dimensional anti-counterfeiting marks or anti-counterfeiting films on the market are all composed of a few images coded by grating and nested, which has weak continuous three-dimensional vision and even several jumping vision channels, weak detail performance, and limited processing area, and cannot produce high-resolution small-area three-dimensional holographic anti-counterfeiting marks or anti-counterfeiting films. SUMMARY

[0005] In view of the shortcomings of the prior art, the present application provides a high-resolution color holographic structure preparation method, which separates, optimizes, fuses and encodes multi-angle sampling image array data, and finally converts the encoded data into surface relief micro-nano structure by micro-nano processing. The anti-counterfeiting product produced based on the method can be molded and ironed, has strong three-dimensional imaging space, bright colors, strong detail performance, is extremely attractive, and has high difficulty in image design algorithm and high difficulty in imitation, and has strong anti-counterfeiting performance.

[0006] To achieve the above technical solutions, the present application provides a high-resolution color holographic structure preparation method, which specifically includes the following steps:

[0007] S1, obtaining three-dimensional information of an object: multi-angle image sampling of the object is performed by photographing the real object with a camera or by three-dimensional modeling with modeling software, to obtain a color image array Pn containing multi-angle information of the object;

[0008] S2, extracting and separating three primary color channels of the image array Pn, wherein R represents a red channel, G represents a green channel, and B represents a blue channel;

[0009] S3, setting a convolution kernel h(k1, k2), and respectively convolving each color channel component in R, G and B of each image to perform image data optimization and region extraction to obtain a three-channel convolution extraction data block set r' i , g' i , b' i ;

[0010] S4, respectively pooling r' i , g' i , b' i data, reducing the dimension of each channel data block (x, y) of each image to 1, and normalizing the value range to [0, 1] weight matrix, and the processed data set is denoted as r″ i , g″ i , b″i ;

[0011] S5, using addressing method, respectively extracting r" i , g" i , b" i The values of each same coordinate position in the set are integrated to obtain data matrices R", G", and B";

[0012] S6, according to the grating equation mλ=d(sinθ), three different period surface relief gratings are designed, wherein m is the diffraction order, λ is the wavelength of light, d is the grating period, β is the diffraction angle, the designed three-color grating periods are T R , T G , and T B , rotating T R , T G , and T B grating direction, respectively corresponding to different viewing angles in the R, G, and B matrices of the image array Pn, the rotation grating direction angle is [α1, α2, …, α n ], and rα n , gα n , and bα n respectively represent the RGB three-channel encoding gratings of the nth viewing angle image, and the three-channel encoding gratings of the total sampling visual image are represented as RT α , GT α , and BT α ;

[0013] S7, respectively multiplying the R", G", and B" matrices with the RT α , GT α , and BT α grating data to obtain the encoded three-color channel data matrices R2, G2, and B2;

[0014] S8, re-fusing the encoded three-color channel data R2, G2, and B2 to obtain the micro-nano processing data file F of the high-resolution color holographic structure;

[0015] S9, manufacturing the data file F into a moldable subwavelength level surface relief structure through micro-nano processing.

[0016] Preferably, in the step S1, the number n of the minimum image sampling of the color image array Pn is calculated by the following formula:

[0017]

[0018] Wherein r is the rotation radius, θ is the visual sampling range value, the θ range value is -90°-90°, and σ is the resolution of the human eye.

[0019] Preferably, in the step S2, the data sets of the color image array Pn after three-color separation are respectively denoted as:

[0020] R = [R1, R2, …, Rn]

[0021] G = [G1, G2, …, Gn]

[0022] B = [B1, B2, …, Bn]

[0023] Preferably, in the step S3, the convolution kernel h(k1, k2) with a kernel radius of (r k1 = (k1-1) / 2, r k2 = (k2-1) / 2) where k1 and k2 are odd numbers, is used to further optimize the image data and extract the regions:

[0024]

[0025] where r' i , g' i , b' i are the data block sets of the three channels of the i-th image extracted by convolution, and (x, y) is the data block coordinate.

[0026] Preferably, in the step S6, the three-channel encoded raster representation of the entire sampled visual image is:

[0027] RT α = [rα1, rα2, …, rα n ]

[0028] GT α = [gα1, gα2, …, gα n ]

[0029] BT α = [bα1, bα2, …, bα n ]

[0030] The raster arrays RT α , GT α , BT α all have a width of W and a height of H, and the width of each three-channel encoded raster is w = W / n and the height is h = H.

[0031] Preferably, in the step S6, the three-color raster periods T R , T G , T B are in the range of 0.1 μm to 2 μm, where 0.1 μm < T R < 0.9 μm, 0.5 μm < T G < 1.4 μm, 1 μm < T B < 2 μm.

[0032] Preferably, in the step S6, the grating direction angle [a1, a2, …, aN] is designed as an arithmetic sequence, and the common difference is n ] is an arithmetic sequence, and the common difference is The numerical range is -90° to 90°.

[0033] Preferably, in the step S7, the obtained encoded three-color channel data matrix is:

[0034]

[0035] Preferably, in the step S8, the obtained micro-nano processing data file F of the high-resolution color holographic structure is:

[0036]

[0037] Wherein,

[0038] Preferably, in the step S9, the sub-wavelength level surface relief structure has a depth of 0.05-0.35 μm.

[0039] The high-resolution color holographic structure preparation method provided by the present application has the following beneficial effects:

[0040] 1) The present application separates, optimizes, fuses and encodes the multi-angle sampling image array data, and finally converts the encoded data into a surface relief micro-nano structure by micro-nano processing. The anti-counterfeiting product made based on this method can be molded and hot stamped, has strong three-dimensional imaging space, bright colors, strong detail performance, and is extremely attractive.

[0041] 2) The image processing technology provided by the present application makes the encoding grating quantity and the imaging size not increase with the increase of the number of images, has strong information capacity, makes the small-size anti-counterfeiting mark or anti-counterfeiting film also have strong detail performance, and has high difficulty in image design algorithm and high difficulty in imitation, and has strong anti-counterfeiting performance.

[0042] 3) The anti-counterfeiting product provided by the present application has a naked eye 3D effect, and the spatial visual effect is formed based on the change of horizontal and vertical period micro-patterns, can form complex three-dimensional micro-patterns, and has good anti-counterfeiting effect. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The flowchart of the present application.

[0044] Figure 2 It is a schematic diagram of the principle of calculating the minimum number of image samples.

[0045] Figure 3is the schematic diagram of extracting and separating the three primary color channels of the multi-view image array Pn.

[0046] Figure 4 is the schematic diagram of convolution extraction and pooling dimension reduction of the color channel data of each visual image R.

[0047] Figure 5 is the schematic diagram of encoding the image array containing different views using different direction angle gratings.

[0048] Figure 6 is the schematic diagram of the structure morphology of different encoding grating arrays corresponding to different numbers of sampled images.

[0049] Figure 7 is the schematic diagram of fusing the encoded RGB three color channels to form a micro-nano processing file.

[0050] Figure 8 is the schematic diagram of the structure after the micro-nano processing file (x, y) position data block is processed into a surface relief. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0052] Embodiment: a high-resolution color holographic structure preparation method.

[0053] Referring to Figures 1 to 8 , a high-resolution color holographic structure preparation method specifically includes the following steps:

[0054] (1) Obtain three-dimensional information of an object: use a camera to take pictures of a real object or use modeling software (such as 3Dmax, Maya, etc.) to perform three-dimensional modeling to sample images of the object from multiple angles, to obtain a color image array Pn containing multi-angle information of the object; the maximum value of n is determined according to actual needs, and the more image information collected, the more delicate the final displayed spatial stereoscopic image will be. However, due to the limited performance of computers, in order to ensure the efficiency of data processing, the amount of sampling data, i.e., the number of images, should not be too large. According to the three-dimensional holographic diffraction imaging principle, the minimum image sampling quantity n can be calculated by the following formula:

[0055]

[0056] Wherein r is the visual sampling rotation radius (i.e. the imaging suspension highest point), θ is the visual sampling range value, and σ is the human eye resolution. Through the design of the above minimum image sampling quantity n, the data processing efficiency can be improved as much as possible under the premise of meeting the fine spatial stereoscopic image, and the use of too complex is avoided.

[0057] As Figure 2 When the object reconstruction image suspension height r = h1, the same space multi-vision information imaging can be well connected. When the sampling rotation angle is unchanged, the imaging suspension height is increased to r = h2, and under the condition that the number of visual sampling images is not increased, the reconstructed holographic image appears the phenomenon of incoherent jump. Therefore, the larger the visual sampling rotation radius, i.e. the higher the imaging suspension height, the larger the visual sampling range, and the more the number of images required to be collected.

[0058] According to the average value of the limit of human eye resolution, σ = 30 μm is set in the embodiment, the sampling rotation angle θ = 30° is set, and the reconstruction image imaging height is r = 5 mm. It is calculated from the above formula that the number of images required to be collected is at least n = 88.

[0059] (2) Extract and separate the image array Pn three color channels, as Figure 3 shown, wherein R represents the red channel, G represents the green channel, and B represents the blue channel. The data sets after three-color separation are respectively:

[0060] R = [R1, R2, …, Rn]

[0061] G = [G1, G2, …, Gn]

[0062] B = [B1, B2, …, Bn].

[0063] (3) Set the convolution kernel h (k1, k2), and respectively convolve each color channel component in R, G and B of each image, as Figure 4 shown, to perform image data optimization and region extraction:

[0064]

[0065] Wherein r' i , g' i , b' i are the data block sets of the three channels of the i-th image extracted by convolution, and (x, y) is the data block coordinates. The purpose of image data optimization and region extraction by component convolution is to enhance image details, so that the processed image has strong detail performance.

[0066] In the embodiment, the set value of h is h (3, 3), i.e. a 3-row 3-column matrix, which plays a role of data block extraction and image detail enhancement:

[0067]

[0068] (4) respectively for r' i g' i b' i The data is pooled, reducing the dimension of each data block (x, y) per channel of each image to 1, and normalizing the values ​​to a weight matrix within the range of [0, 1]. The processed dataset is r″. i g″ i b″ i .

[0069] The pooling method employed is one of the following: max pooling, mean pooling, random pooling, median pooling, fractional max pooling, and combined pooling. This primarily achieves data dimensionality reduction and enhances the anti-interference performance of the information. The weight matrix mainly affects the duty cycle of the coded gratings for different channels in different visual images, resulting in strong spatial depth and vibrant colors. This embodiment uses the mean pooling method.

[0070] (5) Extract r″ using the addressing method. i g″ i b″ i The values ​​at each identical coordinate position in the set form a new dataset, such as for r″. i After processing, we get:

[0071] R″ 11 =[r″1(1,1),r″2(1,1),^……,r″ n (1,1)]

[0072] R″ 12 =[r″1(1,2),r″2(1,2),^……,r″ n (1,2)]

[0073] ...

[0074] R′ xy =[r″1(x,y),r″2(x,y),^……,r″ n (x,y)]

[0075] We obtain matrix R":

[0076]

[0077] Similarly, we can obtain:

[0078]

[0079] (6) Based on the grating equation mλ=d(sinβ), three surface relief gratings with different periods were designed, where m is the diffraction order, λ is the wavelength, d is the grating period, and β is the diffraction angle. The designed three-color gratings have periods T0, T1, and T2, respectively. R T G T B Rotate T R T G T B The grating directions correspond to different viewpoints in the R, G, and B matrices of the three primary colors in the image array Pn. The rotation grating direction angles are designed to be [α1, α2, ..., α]. n ], using rα n gα n bα n Let represent the RGB channel coded raster of the nth viewpoint image. Then, the three-channel coded raster of the entire sampled visual image is represented as:

[0080] RT α =[rα1,rα2,……,rα n ]

[0081] GT α =[gα1,gα2,……,gα n ]

[0082] BT α = [bα1,bα2,……,bα n ].

[0083] like Figure 5 The image shown is an example of a color channel encoding grating, [α1, α2, ..., α n [A] is an arithmetic sequence with a common difference of . Given n visual images, a three-channel grating array with R, G, and B channels, each with a total width of W and a height of H, then the width of each visual image's three-channel encoding grating is w = W / n, and the height is h = H. Higher equipment processing precision requires smaller total grating array widths W and H; a larger number of sampled visual images results in smoother grating array structure connections. When the number of sampled visual images approaches infinity, the overall grating array structure takes on a standard circular arc shape. For example... Figure 6 As shown, when n=2, n=4, n=8 and n=∞, the overall structure of the grating array becomes smoother and has a very strong information storage capacity.

[0084] In this embodiment, the diffraction order m = 1, the diffraction angle β = 30°, and the red wavelength corresponding to the red channel data R is taken as λ. R =630×10 -3 μm, the green light wavelength corresponding to the green channel data G is taken as λ G =530×10 -3μm, the blue light wavelength corresponding to blue channel data B is taken as λ B =430×10 -3 μm, the corresponding coding grating periods for the three color channels are calculated to be T. R =1.26μm, T G =1.06μm, T B =0.86μm.

[0085] In this embodiment, the number of sampled images n = 88, the object sampling rotation angle θ = 30°, and correspondingly, the encoding grating rotation angle α1 = -15°. 88 =15°, and the rotation angle interval of each visual coding grating is calculated to be Δα = 0.34°.

[0086] In this embodiment, a grating array RT is set. α GT α BT α With a width of W = 60 μm and a height of H = 20 μm, the width of the coded grating for each visual three-channel was calculated to be w = 0.682 μm and the height to be h = H = 20 μm.

[0087] Through the above image processing technology, the number of coded gratings and the imaging area do not increase with the increase of the number of images, and have a strong information carrying capacity, so that even small-format anti-counterfeiting labels or anti-counterfeiting films have extremely strong detail expression.

[0088] (7) Connect the three primary color matrices R”, G”, B” respectively with RT α GT α BT α The raster data is multiplied by a dot to obtain the encoded three-color channel data matrix:

[0089]

[0090] By combining the three primary color R”, G”, B” matrices with RT α GT α BT α Multiplying raster data by a dot can increase the algorithmic difficulty of image design, make it more difficult to counterfeit, and greatly enhance the anti-counterfeiting performance of images.

[0091] (8) Re-fuse the encoded three-color channel data R2, G2, and B2, as follows: Figure 7 As shown, the micro / nano fabrication data file F of the high-resolution color holographic structure was obtained:

[0092]

[0093] in

[0094] By re-fusing the coded three color channel data R2, G2, B2, the stereoscopic sense of the re-fused imaging space can be greatly enhanced, and the color of the imaging is more colorful, and the detail performance of the imaging space is greatly enhanced.

[0095] (9) The data file is made into a moldable sub-wavelength surface relief structure by micro-nano processing method, such as Figure 8 As shown in f xy An example of the appearance of the data converted into the surface relief structure. In this embodiment, the sub-wavelength surface relief structure has a depth of 0.1-0.2 μm.

[0096] The present application separates, optimizes, fuses and encodes the multi-angle sampling image array data, and finally converts the coded data into a surface relief micro-nano structure by micro-nano processing method. The anti-counterfeiting product made based on this method is moldable and hot stampable, has strong stereoscopic sense, bright color, strong detail performance, and is extremely attractive. Moreover, the image processing technology provided by the present application makes the coded grating quantity and the imaging size not increase with the increase of the number of images, has strong information capacity, and makes the small-size anti-counterfeiting mark or film also have strong detail performance. In addition, the image design algorithm prepared by this method is difficult to copy, has strong anti-counterfeiting performance.

[0097] The above is only a preferred embodiment of the present application, but the present application should not be limited to the disclosed content of the embodiment and the drawings, so any equivalent or modification made without departing from the disclosed spirit of the present application falls within the protection scope of the present application.

Claims

1. A method for preparing a high-resolution color holographic structure, characterized in that, Specifically comprising the following steps: S1, obtaining three-dimensional information of the object: using a camera to take a real object or using modeling software to perform three-dimensional modeling to sample images of the object from multiple angles to obtain a color image array containing multi-angle information of the object ; S2, extracting and separating the image array three primary color channels, where R represents a red color channel, G represents a green color channel, and B represents a blue color channel; S3, set the convolution kernel h(k1, k2), respectively, for each color channel component of each image R, G, B convolution, image data optimization, region extraction to get three channel convolution extraction data block set , , ; S4, respectively, to , , pool the data, reduce the dimension of each data block (x, y) of each channel of each image to 1, and normalize the value range to [0, 1] weight matrix, the processed dataset is denoted as ; S5, using addressing method, respectively extract The data matrix is obtained by integrating the values of each same coordinate position in the set R'' 、 G'', B'' ; S6、According to the grating equation , three different period surface relief gratings are designed, where m is the diffraction order, , λ is the wavelength of light, d is the grating period, β is the diffraction angle, the designed three-color grating periods are 、 , the rotation grating direction corresponds to different viewing angles in the image array of the three primary colors 、 、 in the matrix, the rotation grating direction angle is designed as [ ], and represents the RGB three-channel encoded grating of the nth viewing angle image, and the three-channel encoded grating of all sampled visual images is represented as 、 、 ; S7, respectively, the three primary colors , , matrix and , , dot product of the grating data, get the three color channel data matrix R2, G2, B2 after encoding; S8, re-fuse the encoded three color channel data R2, G2, B2 to obtain the micro-nano processing data file F of high-resolution color holographic structure; S9, make the data file F into a moldable sub-wavelength level surface relief structure by micro-nano processing.

2. The method of claim 1, wherein the step of exposing the photoresist layer to the laser beam is performed by a scanning method. The number n of minimum image samples of the array of color images is calculated by the following formula: where r is the radius of rotation, is the value of the visual sampling range, the range value is ~90 , is the resolution of the human eye.

3. The method of claim 1, wherein the step of exposing the photoresist layer to the laser beam is performed by a scanning method. In the step S2, the color image array The data sets after the three-color separation are respectively denoted as: R = [R1, R2, ……, Rn]; G = [G1, G2, ……, Gn]; B = [B1, B2, ……, Bn].

4. The method of claim 1, wherein the step of exposing the photoresist layer to the laser beam is performed by a scanning method. In the step S3, the convolution kernel h(k1, k2) with the kernel radius where k1, k2 are odd numbers, and then image data optimization and region extraction are performed: ； ； ； wherein , , is the set of data blocks of the i-th image of the three channels of the convolutional extraction, (x, y) are the data block coordinates.

5. The method of claim 1, wherein the step of exposing the photoresist layer to the laser beam is performed by a scanning method. In the step S6, all the sampling visual image three-channel encoded raster representation is: =[ ]； =[ ]； =[ ]； Optical grating array The width of each visual channel is w = W / n, and the height of each visual channel is h = H.

6. The method of claim 5, wherein the step of exposing the photoresist layer to the laser beam is performed by a scanning method. In the step S6, the tricolor grating period , The value range is wherein , , .

7. The method of claim 5, wherein the step of exposing the photoresist layer to the laser beam is performed by a scanning method. The step S6 designs the grating direction angle ] as an arithmetic sequence, and the tolerance is , and the numerical range is -90 to 90 .

8. The method of claim 1, wherein the step of exposing the photoresist layer to the laser beam is performed by a laser beam having a wavelength of 193 nm or less. In the step S7, the obtained encoded three color channel data matrix is: ； ； 。 9. The method of claim 1, wherein the step of exposing the photoresist layer to the laser beam is performed by a laser beam having a wavelength of 193 nm. In the step S8, the obtained micro-nano processing data file F of high-resolution color holographic structure is: ; wherein, .

10. The method of claim 1, wherein the method further comprises: In the step S9, the depth of the sub-wavelength level surface relief structure is between 0.05µm and 0.35µm. ​

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