Fuzzy retina fundus image enhancement method based on generative adversarial network

Abstract

The invention relates to a fuzzy retina fundus image enhancement method based on a generative adversarial network, and belongs to the technical field of image enhancement. The method comprises the following steps of 1, constructing a training set and a test set; 2, respectively preprocessing the fundus images in the training set and the test set constructed in the step 1; 3, constructing a generative adversarial network model for enhancement; and 4, enhancing the fuzzy retina fundus image of the test set by using the trained generator to obtain a final enhancement result. Compared with an existing fundus image enhancement method, the method has the advantages that the enhancement result is smooth and noiseless, the color restoration degree is high, and the problems of color offset, overhigh contrast degree, color distortion and noise amplification of a traditional method are effectively solved; a complex prior model is prevented from being designed, and the processing speed is higher; the details, such as blood vessel distribution of the retinal fundus image, etc., are well restored, and the generation result is real and reliable.

Description

technical field [0001] The invention relates to a fuzzy retinal fundus image enhancement method based on a generation confrontation network, and belongs to the technical field of image enhancement. Background technique [0002] Fundus images of the retina are used by ophthalmologists to diagnose a variety of retinal disorders. Due to defects in the imaging process or ocular lesions, some fundus images are of poor quality, mainly including blurred images, incorrect focus, uneven illumination, and low contrast. The obtained fundus images cannot meet the needs of clinical diagnosis, and are not suitable for computer-aided diagnosis of retinal diseases. Therefore, it is necessary to improve the anatomical structure visibility of these images so that the processed image quality is suitable for further clinical diagnosis and intelligent processing needs. [0003] Existing studies have proposed some grayscale or color retinal image enhancement methods, which can be roughly divide...

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Problems solved by technology

[0005] The purpose of the present invention is to address the following two defects in the existing retinal fundus image enhancement methods: 1) the obtained color fundus image loses important image features and color information, and while the contrast is improved, the local noise of the fundus image is also eliminated. Enhancement is not conducive to accurate diagnosis of diseases by ophthalmologists; 2) Most of them use prior knowledge, the network model is complex, and the method needs to be adjusted manually according to the characteristics of the data, and the scalability is weak; a fuzzy retinal fundus image enhancement based on generative confrontation network is proposed method

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