Image crowd fusion mechanism based on optical embedded system
© The Author(s). 2016
Received: 1 August 2016
Accepted: 27 September 2016
Published: 12 October 2016
Optical principle embedded image analysis can effectively improve the accuracy of image recognition, but there is a problem of low efficiency and high computational complexity. In view of the above problems, we design an image fusion mechanism based on an optical embedded scheme. We have proposed the optical image space, which is a three-dimensional space coordinate system. Each sample point of the system represents a spot. Light points in the image are described using three quantities, which are light intensity, image concentration, and light. An embedded optical image analysis model is proposed. We use a mobile embedding scheme to map multiple points of light into the same optical surface. Optical crowd block structure was proposed for increasing image data in an optical image system. The structure can improve the continuity of the image regions in different coordinate systems. The mechanism of crowd fusion for mobile embedded images is proposed. The experimental results show that the proposed mechanism is superior in the aspects of image recognition accuracy and algorithm execution cost.
KeywordsImage fusion Crowd Optical system Embedded control
Optical image analysis  is kind of the bottom of the image processing technology. The technique is based on the image of the specific optical characteristics of  image is defined as a number of independent image range. It is widely used in image transmission, monitoring and identification, and video processing . However, the optical principle of embedded image analysis can effectively improve the accuracy of image recognition , 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 . Algorithms were designed by Gong et al.  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  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.  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  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  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 .
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.
2 Optical image embedded analysis model
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.
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.
3 Mobile embedded image fusion mechanism
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.
4 Fusion algorithm analysis and verification
Number of frames
Size of image
[2, 10] M bytes
[0.38, 0.76] micrometer
[30, 90] radian
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.
XM and ML conceived and designed the study. XM performed the experiments and wrote the paper. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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