Image enhancement method and system based on anisotropic gradient model

Abstract

The invention relates to an image enhancement method and system based on an anisotropic gradient model. The method comprises the steps of obtaining a to-be-processed image; decomposing the to-be-processed image by adopting an L1-L0 mixed regularized anisotropic gradient layer decomposition model to obtain a fidelity term, a texture layer regularization term and a base layer regularization term; according to the fidelity item, the texture layer regularization item and the base layer regularization item, determining base layer information and texture layer information by utilizing a split Bregman iteration principle; performing contrast adjustment on the base layer information and the texture layer information to obtain adjusted base layer information and adjusted texture layer information;and determining an enhanced image according to the adjusted base layer information and the adjusted texture layer information. By means of the method and the system, the basic layer information and the texture layer information can be protected when the image layer is decomposed, and image contrast enhancement is effectively achieved.

Description

technical field [0001] The invention relates to the technical field of image enhancement, in particular to an image enhancement method and system based on an anisotropic gradient model. Background technique [0002] At present, photoelectric imaging systems are widely used in many fields such as outdoor information perception, target detection and tracking, and intelligent systems. In the process of acquiring images, the imaging system inevitably encounters the situation of low ambient illumination, which makes the contrast of the target image after imaging low, which affects the later analysis and use of the image. [0003] A better means of image contrast enhancement is based on image layer decomposition, that is, the image is decomposed into base layer and texture layer, and then the two are processed separately to achieve the purpose of image information contrast enhancement. At present, the more popular layer decomposition contrast enhancement method is based on the me...

AI Technical Summary

Problems solved by technology

Although the total variational model can effectively enhance the contrast of the image and enhance the edge features of the image to a certain extent, the ladder effect of the L1 regularized total variational model will make the edge of the image base layer have a rectangular structure, such as image The sharp edge and corner features become smooth, which partially distorts the basic information of the processed image; while the L0 regularized total variation model can protect the texture layer information of the image to a certain extent, but the L0 total variation model is to uniformly control the image texture. The total number of non-zero gradients in two directions of the layer, the non-zero image gradient distribution information in a single direction cannot be known, that is, the graphic information of each direction of the texture layer cannot be protected separately, resulting in the protection of the decomposed image texture layer information insufficient

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