Retina image parallel processing method and device
A processing method and retinal technology, applied in the field of image processing, can solve the problems of unguaranteed model generalization performance and poor retinal image processing effect, etc.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2020-11-13
Smart Images

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Abstract
Description
technical field
[0001] The invention belongs to the technical field of image processing, and in particular relates to a retinal image parallel processing method and device. Background technique
[0002] At present, most of the processing of relevant objects in retinal images is manually designed. This method has great limitations both in accuracy and in objectivity. Thanks to the rapid development of computer software and hardware technology and the maturity of computer vision technology, researchers are trying to find an efficient and intelligent way that can automatically process the relevant features of retinal images without subjective interference, so as to provide a basis for the corresponding application fields. , such as biometric identification technology to provide more reliable and effective technical protection. Lin Jipeng (Lin Jipeng, Research on Key Technologies of Fundus Retina Image Processing and Analysis [D], Huaqiao University, 2019.) uses the wavelet do...
Examples
Embodiment Construction
[0075] The present invention will be further explained below in conjunction with accompanying drawing and specific embodiment:
[0076] The present invention first adopts the chaotic supply and demand algorithm to optimize the objective function (determined according to the specific requirements of image enhancement) to determine the undetermined parameters of the transformation function so as to enhance the collected retinal images; then perform traditional transformation on the enhanced retinal images according to an appropriate order (rotate, flip, increase contrast, translate, etc.), and then use the generated confrontation network and its variants to synthesize the image through confrontation training with the previous retinal image as the real image, and then use the above image as the real image input to generate the multi-layered model and virtual-real interaction Decompose the joint training model composed of interval-type two intuitionistic fuzzy convolutional neural ...