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Section 2 describes the optical image embedded analysis model.
Image embedded analysis process cannot effectively solve the image forming problem.
The generated image interval number can be reduced by moving embedded analysis with image fusion.
From the comparison results, it is found that the process of image embedded analysis cannot solve the problem of image forming.
Fig. 1 Spot transfer in image embedded analysis left{begin{array}{l}{mathrm{LI}}_{mathrm{spot}}=frac{1}{D_1+{displaystylepha {disum_{i=1}^4{mathrm{LI}{mathrm{LI}_i+betaa {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.
The embedded analysis model based on an optical image has many advantages, but it also brings some disadvantages.
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Tissue was sent to histopathology for wax embedding, analysis and dating using light microscopy.
Spectral embedding analysis showed that there was a strong relationship between spatial and temporal organization.
In order to facilitate comparison and analysis, we statistically analyze the original image, the embedded image analysis results, and the results of the proposed ICF-OE algorithm.
Thus, the entire structure is embedded into analysis and synthesis filter banks.
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.
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