Image crowd fusion mechanism based on optical embedded system

Optical image analysis [1] is kind of the bottom of the image processing technology. The technique is based on the image of the specific optical characteristics of [2] image is defined as a number of independent image range. It is widely used in image transmission, monitoring and identification, and video processing [3]. However, the optical principle of embedded image analysis can effectively improve the accuracy of image recognition [4], but there is the problem of low efficiency and high computational complexity.

About optical embedded scheme, the fabrication of planar multimode waveguides within thin glass foils based on a two-step thermal ion exchange process was reported on article [5]. Algorithms were designed by Gong et al. [6] for both transparent and opaque virtual optical network embedding over flexible-grid elastic optical networks.

About the image fusion, an information-based approach was proposed in article [7] for assessing the fused image quality by the use of a set of primitive indices which can be calculated automatically without a requirement for training samples or machine learning. An area-based image fusion algorithm was presented by Byun et al. [8] to merge synthetic aperture radar and optical images. The mid-wave infrared, long-wave infrared, and dual-band images were obtained and evaluated in article [9] from a voltage tunable quantum dot focal plane array. The novel tone mapping algorithm based on fast image decomposition and multi-layer fusion was proposed in article [10] for solving the low efficiency and color distortion in several typical tone mapping operators for high dynamic range images. The multimodal fusion framework was presented by using the cascaded combination of stationary wavelet transform and nonsubsampled transform domains for images acquired using two distinct medical imaging sensor modalities [11].

The rest of the paper is organized as follows. Section 2 describes the optical image embedded analysis model. We give the mobile embedded image fusion mechanism in Section 3. In Section 4, the fusion algorithm analysis and verification was completed. Finally, the paper was concluded in Section 5.

Optical image space is a three-dimensional space coordinate system. Each sample point of the system represents a spot. Light points of the image were defined by three quantities: light intensity, image concentration, and light. The distortion degree of the spot is very important for the identification and the distributed transmission of the optical image. Suitable optical image space can provide the accurate and abundant information.

In general, the distortion of light spot is a basic condition and essential feature of an object, which could be extracted accurately from the complex environment. At the same time, the distortion of the spot will seriously affect the accuracy and integrity of the image perception. The composition of the light spot is formed by mixing the three datums of the arc, the intensity, and the concentration. When the image was analyzed, the basic optical space must be based on the above three datums. The influence analysis of other optical reference spatial factors can generally use the formula from radians, strength, and concentration of conversion, such as nonlinear concentration curve of linear transformation and fiber index for conversion.

Figure 1 shows the change process of the light spot in two different states. The light spot of state 1 is mainly composed of four direction embedding. The light spot of state 2 is mainly embedded in eight directions. The diversity performance of the three baseline elements includes a spot of light in each direction. The square in Fig. 1 represents a direction image vector of the light spot. In the box, the black dot indicates the spot in the direction of the reference vector elements of diversity. Formula (1) solved the problem of light intensity LI_spot acquisition of light spot. The surface integral and nonlinear transformation of the spot image concentration IC_spot could be obtained by formula (2). Light curve LR_spot can be obtained by formula (3).

$$ \left\{\begin{array}{l}{\mathrm{LI}}_{\mathrm{spot}}=\frac{1}{D_1+{D}_2}\left(\alpha {\displaystyle \sum_{i=1}^4{\mathrm{LI}}_i+\beta {\displaystyle \sum_{j=1}^8{\mathrm{LI}}_j}}\right)\hfill \\ {}{\mathrm{LI}}_4=\sqrt{D_1}\hfill \\ {}{\mathrm{LI}}_8=\sqrt{D_2}\hfill \end{array}\right. $$

(1)

Here, H represents the 3D space of the interval optical image coordinate set. k represents the space occupied by the spot.

Here, D _R represents the red data vector. D _G represents the green data vector. D _B represents the blue data vector. The above parameters can be obtained by calculating D _C.

Here, RF is the optical fiber refractive index. λ _x represents the X direction of the light wavelength. λ _y represents the Y direction of the light wavelength. λ _z represents the Z direction of the light wavelength.

The embedded analysis model based on an optical image has many advantages, but it also brings some disadvantages. The embedding relation between the spot and the spot is neglected. Image embedded analysis process cannot effectively solve the image forming problem. In view of these shortcomings, we use a mobile embedding scheme to map multiple light points into the same optical surface. This can effectively reduce the embedding relationship between the ratio of redundant points and the fusion point. Optical crowd block structure was added into the optical image system. The structure can improve the continuity of the image regions in different coordinate systems. The crowd block can weaken the fine difference between different coordinate systems and the distortion redundancy area. The generated image interval number can be reduced by moving embedded analysis with image fusion.

On the basis of the above mechanism, the optical image embedded optimization analysis model and algorithm structure are shown in Fig. 2. The four states cannot be converted to each other in Fig. 2. This updating process is helpful to balance the embedded iterative process and the computation of image mining. The crowd fusion scheme is helpful to improve the performance of the algorithm in order to increase the performance of the algorithm.

In order to improve the execution efficiency and reduce the computational complexity, we design an image fusion mechanism based on optical embedded system. First of all, based on the three-dimensional space of the optical image in the interval coordinates system, each sample point is defined as a light point. The light spot is defined as the intensity of illumination, the concentration of the image and the curve of the light. An embedded optical image analysis model is proposed. Secondly, based on the moving embedding scheme, the optical surface of multiple light points is integrated. Optical crowd block structure was used to increase the image data in optical image system. The mechanism of crowd fusion for mobile embedded images is proposed. Finally, compared with the embedded image analysis mechanism, the proposed mechanism has the advantages of high recognition accuracy, low computational complexity, and low redundancy.