A video image de-fog enhancement method based on improved convolution matching tracking pipeline

Abstract

The invention relates to an improved convolution matching tracking pipeline video image defogging enhancement method. The fog layer model is obtained by processing the center circle in the video framein the pipeline, and the fog layer is used as the source of convolution dictionary to train the dictionary. The background image and foreground image are obtained by training the low rank matrix decomposition of the first few frames, the residual image is initialized according to the foreground image and the feature response is calculated through the residual image, the foreground reconstructed image is initialized, and the noise energy is calculated. Maximum search for feature response is performed; Reconstruction of foreground image using maximum value and current maximum feature response and updating feature response using maximum value are performed; the residual image is updated, and the residual image energy is calculated. If the residual image energy is less than the noise energy,the final defogging image is calculated. The invention can effectively obtain the fog layer model, and find the fog layer coincident with the video frame through the fog layer model as a convolution dictionary and remove the fog layer. the method can be used in pipeline video image enhancement and other scientific fields.

Description

technical field [0001] The present invention relates to the technical field of pipeline video image processing, in particular to an improved convolution matching tracking pipeline video image defogging enhancement method. Background technique [0002] As an important link before pipeline detection, pipeline video image defogging enhancement has become a hot research issue in pipeline video image processing. Pipeline video image defogging enhancement is to highlight or suppress certain information in the image according to specific needs, so as to achieve the purpose of enhancing useful information. In the pipeline inspection system, the defogging enhancement effect of the pipeline video image plays a decisive role in the subsequent pipeline crack detection and damage detection. At present, pipeline video image defogging methods mainly include image enhancement methods based on Retinex, histogram equalization image enhancement methods, image enhancement methods based on dark...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems existing in the current convolution matching tracking video image enhancement, in order to overcome excessive defogging and incomplete defogging, and improve the accuracy of video image defogging, the present invention proposes a video image dehazing method based on an improved convolution matching tracking pipeline. Fog enhancement method, which can effectively dehaze the whole image according to the extracted fog layer model, improve the robustness of the fog layer model and the visibility of the defogging effect, and is suitable for scientific fields such as pipeline video image enhancement

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