An Integrated Imaging Display Content Generation Method Based on Layered Fusion for Optimized Depth of Field

Abstract

This invention discloses a method for generating integrated imaging element image arrays based on adaptive depth layer fusion for depth optimization under monocular vision. It mainly comprises three parts: adaptive depth layering, layer-based generation of element image arrays, and fusion generation of the final element image array. First, this invention utilizes depth information from the depth map and display device parameters to jointly construct an adaptive layering model, dividing the 3D scene into multiple depth layers. Next, the information of each layer is encoded to generate a corresponding element image array. Finally, referencing the human visual system and depth cues, a Gaussian fusion coding method with depth differences is used to fuse the element image arrays from different depth layers, generating the final element image array. The element image array generated by this invention can extend the high-quality display depth range within a given depth boundary, improving the 3D visualization display quality of the integrated imaging system.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional image processing, particularly the field of integrated imaging display content generation, and specifically provides a method for generating integrated imaging element image arrays based on adaptive depth layer fusion to optimize depth of field. Background Technology

[0002] Integrated imaging light field display technology has attracted widespread attention from scholars and enterprises both domestically and internationally due to its simple system structure, lack of the need for auxiliary equipment, and full-color, continuous viewing angle. The generation of element image arrays is a key technical step, particularly important for stereoscopic display of 3D scenes. Currently, generating element image arrays based on the original light field color image and its registered depth map under monocular vision has become a mainstream method, but it suffers from limitations in the depth range of clear imaging. Based on this, this invention, "An Integrated Imaging Element Image Array Generation Method Based on Adaptive Depth Layer Fusion to Optimize Depth," is proposed. Summary of the Invention

[0003] The purpose of this invention is to provide an integrated imaging element image array generation method based on adaptive depth-layered fusion for optimized depth of field. Its core lies in jointly constructing an adaptive layered model using depth information from the depth map and display device parameters to divide the 3D scene into multiple depth layers; encoding each depth layer separately to generate an element image array; and finally, referencing the human visual system and incorporating depth cues, employing a Gaussian fusion coding method with depth differences to fuse the element image arrays from different depth layers, generating the final element image array. This invention can extend the high-quality display depth of field range within a given depth of field boundary.

[0004] The technical solution of this invention is as follows:

[0005] The first step is to build an adaptive hierarchical model: omitting the Y-axis, the model is shown in the attached figure. Figure 1 The coordinate system shown is based on the X-axis. A pixel A(x) in the element image array... eia A light path is emitted from (g), passing through the optical center of the lens and intersecting with two adjacent depth layers Z1 and Z2 at A(x). oe1 Z1), A(x oe2 Let the pixel size in the original scene be P, Z2). I Then it is necessary to ensure that Δx = |x oe1 -x oe2 |>=P I Assuming the distance between two depth planes is ΔZ, then there is a layer distance. This is the key constraint for adaptive hierarchical structure, expressed by the following formula:

[0006]

[0007] where g is the distance between the display lens array and the elemental image array.

[0008] Based on equation (1), the maximum number of layers can be calculated when the display device parameters are fixed, as shown in equation (2):

[0009]

[0010] where D is the display depth range of the display device, as shown in equation (3):

[0011]

[0012] where d c is the center depth value, as shown in equation (4):

[0013]

[0014] where f is the focal length of the lens.

[0015] Next, the layering threshold values can be screened by local maxima in the histogram of the depth map, combined with the constraint condition, and adjacent layers with a spacing less than the constraint distance calculated by equation (1) are merged, to finally determine the effective layering threshold values {Z1, Z2,..., Z N}, where Z1=Z min , Z N =Z max .

[0016] Second step, generating each layer of elemental image array: according to the depth mask of each layer established in the first step, the information in the color image is sequentially segmented into each depth layer, and the corresponding elemental image array is generated for each layer. For the nth layer with a center depth value of Z n , an elemental image array with a single elemental image size of L*L, the geometric mapping relationship between pixel A(x eia ,y eia ) and the original scene image A(x oe ,y oe ) is shown in equation (5), where L0 represents the lens optical center.

[0017]

[0018] Third step, fusion to generate the final elemental image array: first, scale the elemental image array of each layer generated in the second step based on the depth distance, and the scaling formula is as follows:

[0019]

[0020] P D represents the physical size of the display pixels of the display device, and P Dnrepresents the pixel physical size when the element image array is reconstructed at Z n

[0021] Next, the scaled element image array is fused using a Gaussian weight based on depth difference, as shown in equation (7):

[0022]

[0023] wherein, is normalized to ensure that the sum of the weights of all layers is 1. σ n is the Gaussian kernel standard deviation of the nth layer, which controls the weight distribution range of the current depth layer during fusion, as shown in equation (8):

[0024]

[0025] σ0is the initial standard deviation at the central depth plane, which is the reference parameter of the weight distribution, and in the present application σ0≈0.28D.

[0026] The weight calculated according to equation (7) is brought into equation (9) to perform weighted fusion on the element images of each layer, and the final element image array is obtained:

[0027]

[0028] The beneficial effects of the present application are: based on improving the display quality of each layer of element image array, and following the human eye visual perception characteristics, the element image arrays of different depth layers are fused in a reasonable and natural way, the areas with large depth difference are moderately fused, the areas with similar depth are more closely fused, ensuring the continuity and integrity of the fused image, and thus effectively expanding the high-quality display depth range of the system. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is the adaptive layering principle based on depth information in the present application

[0030] Figure 2 is the two-dimensional color map and depth map of the original scene used in the specific implementation of the present application

[0031] Figure 3 is the adaptive layering effect diagram generated in the specific implementation of the present application

[0032] Figure 4 is the element image array generated in the specific implementation of the present application

[0033] Figure 5 ​These are display images showing the effects of computer reconstruction of the element image arrays generated by the present invention and the element image arrays generated by the comparison method at different depth planes in a specific implementation.

[0034] The comparison method is the method disclosed in the invention patent "Wang Yu, Wang Lu. (2025). An integrated imaging display image array generation method based on local depth template matching. CN118982562B." Detailed Implementation

[0035] The following describes in detail the implementation method of the present invention, "An Integrated Imaging Element Image Array Generation Method Based on Adaptive Depth Layer Fusion for Optimizing Depth of Field", with reference to the accompanying drawings.

[0036] In this invention, the original scene's two-dimensional color image and depth map used are shown in the appendix. Figure 2 As shown. The integrated imaging display system consists of 100×100 pixels with a focal length f = 3mm and a width P. L A lens array consisting of closely spaced square lenses with a diameter of 1 mm is used. The distance from the lens array to the display image array is g = 3.5 mm, and the display pixel size is P. D =0.06mm.

[0037] Based on the above parameters, the center depth d of the system can be calculated. c Depth range D and original scene pixels P I size:

[0038]

[0039] The maximum display depth and the minimum display depth are:

[0040] Z max =d c +D / 2=28.56mm

[0041] Z min =d c -D / 2 = 13.44mm

[0042] The minimum interlayer spacing can then be calculated as:

[0043]

[0044] The maximum number of layers is:

[0045]

[0046] In the example, we use appendix Figure 2 The color image and depth map are used to obtain information about the original scene. Combining the histogram of the depth data with the aforementioned constraints, the color image in this example is divided into three layers, as shown in the attached diagram. Figure 3As shown.

[0047] Next, the three layers of information are encoded according to equation (5) to generate an element image array, and scaled according to equation (6).

[0048] Finally, the element image array is subjected to Gaussian fusion with depth differences according to equations (7), (8), and (9) to generate the final element image array.

[0049] Appendix Figure 4 This is the element image array generated in a specific implementation of the present invention, with attached... Figure 5 This is a display effect diagram of the element image array generated by this invention and the element image array generated by the comparison algorithm after computer reconstruction at different depth planes, along with objective technical indicators. It demonstrates the superiority of this invention in high-quality display of depth of field.

[0050] Matters not covered in this invention are common knowledge.

[0051] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An integrated imaging display content generation method based on layered fusion optimization of depth of field, characterized by: The method comprises the following steps: S1, building adaptive depth layer model: according to the depth information in the original scene depth map and the display device parameters, including the physical distance P of the optical center of the adjacent lens L , the distance g between the element image array and the display lens array, and the central display depth d c , jointly building an adaptive layer model, dividing the three-dimensional scene into N layers, and matching the maximum number of layers N of the display device parameters in the layering process max , as follows: where Z max and Z min are the maximum display depth and the minimum display depth that the display device can present, D is the display depth range of the display device, is the minimum depth difference of two adjacent depth layers, which is an important constraint condition in the layering process, as expressed in equation (2), P I is the physical size of a pixel in the original scene; S2, generating each layer element image array: according to the depth mask of each layer established in S1, the information in the color image is sequentially segmented into each depth layer, and each layer is encoded to generate the corresponding element image array EIA n (x eia ,y eia ); S3, the final element image array is generated: referring to the human visual system, the element image arrays of different depth layers are fused by using the Gaussian fusion coding method with depth difference to generate the final EIA, the weight is calculated according to the distance between each layer depth and the central depth surface, the closer the depth value is to the central depth surface, the higher the weight obtained is, and after the weight is normalized, the element images of each layer are weighted and fused, and the final element image array EIA final (x eia ,y eia ), is represented as follows: where w n (x eia ,y eia ) represents the weight obtained when each layer of the elemental image array is fused, and is expressed as follows: wherein, is a normalization of the weights to ensure that the sum of the weights of all layers is 1, σ n is the standard deviation of the Gaussian kernel of the nth layer, controlling the range of the weight distribution of the current deep layer when fused.

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