Image self-adapting enhancement method based on neural net

A technology of self-adaptive enhancement and neural network, applied in the field of image self-adaptive enhancement based on neural network, can solve the problems of complex algorithm and influence of convergence speed, achieve the effect of small calculation amount, meet the requirements of speed, and improve the visual effect

Inactive Publication Date: 2009-06-10
BEIHANG UNIV
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Problems solved by technology

This method recursively determines the enhancement coefficient through the Gauss-Newton algorithm. The algorithm is relatively complicated, and the selection o

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  • Image self-adapting enhancement method based on neural net
  • Image self-adapting enhancement method based on neural net
  • Image self-adapting enhancement method based on neural net

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Embodiment

[0055] The image adaptive enhancement algorithm process proposed by the present invention is as follows: figure 1 shown. Taking the image collected by the truck fault dynamic image detection system (hereinafter referred to as TFDS system) as an example, the experimental verification is carried out. The TFDS system works in an all-weather environment. The dynamic images directly captured by the camera have low contrast, unclear details, and dim brightness. They must be enhanced before further processing. Therefore, the image adaptive enhancement method based on neural network is applied to the TFDS system to verify the effectiveness of the algorithm.

[0056] First select 100 images collected by the TFDS system, calculate their gray mean and standard deviation and normalize them as input vectors for neural network training, then manually enhance the images, and determine the enhancement coefficient A through multiple experiments , the size of B, which will be normalized as th...

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Abstract

The invention belongs to the technical field of image processing and provides a neural network-based image adaptive enhancement method. The method uses a neural network to establish a model of nonlinear mapping between an average value and a standard deviation of an image and the enhancement factor of an original image and a high-frequency component of the image. The method comprises the following steps for image adaptive enhancement: calculating the average value and the standard deviation of the image and obtaining the enhancement factor by establishing the nonlinear mapping model; filtering the average value of the image and obtaining a low-frequency component of the image; obtaining the high-frequency component of the image through a difference value of the original image and the low-frequency component; and superposing the high-frequency component and the original image which are multiplied with the enhancement factors respectively to realize the adaptive enhancement of the image. The image adaptive enhancement method has the advantages of achieving small calculation amount and strong real-time, automatically acquiring the enhancement factor according to the average value and the standard deviation of the image, realizing the adaptive enhancement of the brightness and the contrast of the image, remarkably improving the visual effect of low-contrast and low-brightness images, and laying a foundation for image identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image adaptive enhancement method based on a neural network. Background technique [0002] With the development of digital image processing, the image-based online dynamic detection system has been widely used in scientific research, medicine, industry and biological genetic engineering and other fields. The image-based dynamic detection system captures the high-speed moving objects through the image acquisition system to obtain the transient information of the moving objects, and then analyzes the dynamic images to complete the identification and detection of faults. Therefore, high-quality, high-contrast images play a very important role in the detection and identification of faults. However, in actual situations, the shutter speed and lighting source cannot meet the requirements, and the obtained images have low contrast and dark brightness. The brightness and contr...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 周富强熊瑛
Owner BEIHANG UNIV
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