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A Retina Mechanism Based Grayscale Image Enhancement Method

A gray-scale image and retina technology, applied in the field of computer vision, can solve the problems of image blur, dark area enhancement effect is not good, dark area larger outline can not be effectively recognized, etc., to achieve the effect of enhancing brightness and edge information

Active Publication Date: 2017-10-27
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

Among the above methods: the global processing operator has the same mapping function for the entire image, but the enhancement effect on the dark area is not good; the local processing operator is easy to cause the reversal of the light and dark boundary; the Retinex method needs to perform large-scale filtering on the target image, It is easy to cause image blurring, and excessive bleaching of bright areas causes loss of image information
[0004] In the traditional edge extraction method, the fixed-size Gaussian difference model is used to extract the edge information in the image, but the size applicable to different brightness areas in the image is different, and the fixed-size Gaussian difference model cannot achieve the optimal effect. Detail textures do not respond strongly enough, and larger outlines in dark areas are not recognized effectively

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  • A Retina Mechanism Based Grayscale Image Enhancement Method
  • A Retina Mechanism Based Grayscale Image Enhancement Method
  • A Retina Mechanism Based Grayscale Image Enhancement Method

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Embodiment Construction

[0021] The present invention will be further elaborated below in conjunction with the accompanying drawings and specific embodiments.

[0022] The human visual system will adjust the brightness to light and dark, and the antagonism mechanism based on the center and periphery of the human eye will change the size of the antagonism with the change of contrast, which can ensure a stronger response to details under bright conditions. The dark environment is more suitable for the lower visual acuity condition at this time, based on which the method of the present invention is proposed.

[0023] Example image like image 3 As shown in a, the size of the grayscale image is 859×1155. The flow chart of the specific calculation process of our algorithm is as follows figure 2 As shown, the specific process is as follows:

[0024] Step 1. Simulate the large-scale characteristics of the horizontal cell receptive field, determine the adaptive parameters, and perform a global brightness ...

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Abstract

The invention belongs to the technical field of computer vision, in particular to brightness enhancement and edge enhancement of grayscale images. The specific process includes estimating the adaptive parameters of the global brightness determination algorithm, generating the brightness map of the image, calculating the brightness enhanced image and edge enhancement processing. Firstly, the adaptive parameters are estimated through the brightness distribution of the global dark area; then the global brightness enhancement processing is performed on the image respectively, and the modulation map of the whole picture is obtained by the modulation function, and the brightness enhancement result is calculated; Finally, the edge enhancement is realized based on the adaptive scale Gaussian difference model. The model scale is affected by the contrast, and finally the finer texture information can be enhanced in the bright area, and the larger outline information can be enhanced in the dark area. The invention can effectively enhance the overall brightness and contrast of the gray scale image, and its self-adaptive characteristics can play a good role in enhancing the edge of the light and dark areas.

Description

technical field [0001] The invention belongs to the technical field of computer vision, in particular to brightness enhancement and edge enhancement of grayscale images. Background technique [0002] The information in grayscale images is mainly in terms of brightness. The large-scale brightness areas in these images determine the content information of objects, while the information of lines and points is an important boundary to distinguish between different objects. Therefore, effectively enhancing the brightness of images with low overall brightness plays an important role in target recognition and so on. [0003] At present, the more classic brightness enhancement methods include traditional global processing operators such as gamma transformation, local operators based on local templates, and the improvement of the prototype of the Retinex method based on the human retina mechanism proposed by Edwin.H.Land in 1963. Among the above methods: the global processing operat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
Inventor 李永杰王冲李朝义
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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