Adaptive image target enhancement method based on difference of Gaussian model

Abstract

The invention discloses an adaptive image target enhancement method based on a difference of Gaussian model and belongs to the digital image processing field. In the method, based on a lateral inhibition phenomenon of a biological visual receptive field, the difference of Gaussian (DOG) model of image space domain filtering is established. Target and noise gray level distribution prior knowledge in a water surface image and a constraint relation of excitability and inhibition effect offset are used to select a model parameter so as to reach a local optimal enhancement effect. By using the method in the invention, comprehensive performance of target enhancement, background inhibition and noise filtering is better than the comprehensive performance of a traditional spatial-domain high pass filter. The enhanced image possesses a good visual sense and simultaneously a demand of subsequent motion vector estimation to a correlation operation signal to noise ratio is satisfied.

Description

technical field [0001] The invention relates to an image target enhancement method, in particular to an adaptive image target enhancement method based on a double Gaussian difference model, and belongs to the technical field of digital image processing. Background technique [0002] Large-scale particle image velocimetry is an emerging instantaneous full-field flow velocity measurement technology. It can not only be used to study the turbulent characteristics and time-averaged characteristics of open channels under normal conditions, but its non-contact characteristics have the application potential of river flow monitoring under extreme conditions. However, compared with controlled laboratory conditions, the optical environment of river surface imaging under field conditions is much more complex. First, tracers that meet the requirements of followability usually have a small size. For large-scale rivers with a field of view up to thousands of square meters, tracers often ap...

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Problems solved by technology

Although the gray scale transformation algorithm such as histogram equalization can improve the visual effect of the image, it has a limited effect on the contrast enhancement between the target and the background; and the smoothing filter algorithm such as the median filter not only cannot eliminate the unique optical noise of the water surface, but will weaken the target image. details of

For the optical noise on the water surface, the current practice is mainly to avoid it by choosing a suitable shooting angle, which often brings inconvenience to the subsequent system calibration and flow estimation

The near-infrared imaging method based on the difference in spectral characteristics of water bodies and tracers realizes real-time enhancement of water surface targets from the hardware, but there is still strong background noise interference

Subsequent use of spatial high-pass filtering can suppress the low-frequency background to a large extent, but the fixed convolution template does not consider the distribution and statistical characteristics of the target, background and noise in the local water surface image, and it is difficult to achieve the optimal enhancement effect

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