Improved unsharp masking image reinforcing method based on logarithm image processing model

Abstract

The invention provides an improved unsharp masking image reinforcing method based on a logarithm image processing model, belonging to the filed of image processing. The method comprises the following steps: transforming a gray level-based input image under a conventional model into a gray tone-based image under the logarithm image processing model; carrying out the improved unsharp masking reinforcement on the image; and transforming the image subjected the unsharp masking reinforcement into the gray level-based image under the conventional model. After the unsharp masking image reinforcing method based on the logarithm image processing model is used, the defect of the 'overflowing' of the gray level in the unsharp method under the conversional model is overcome; due to the isomorphic relationship between the operation of the logarithm image processing model and the operation of the conventional process model, the isomorphic transform and the non-isomorphic transform, the method is simpler and more efficient; and the edge region and smooth region of the image are reinforced to different extents by utilizing the gradient information of the image, so that the defect that the whole image is reinforced by the constant factor at the same intensity based on the conversional method is overcome, and the obtained result is better.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an improved unsharp mask image enhancement method based on a logarithmic image processing model. Background technique [0002] Unsharp Masking (Unsharp Masking, UM) is an image edge enhancement method proposed to adapt to the processing of image features of different sizes. This method was first applied in photography technology to enhance the edge and details of the image; optically The operation method is to superimpose the focused positive film and the defocused negative film on the negative film. The result is that the high-frequency components of the positive film are enhanced, thereby enhancing the outline. The defocused negative film is equivalent to a "fuzzy" template (mask), which is different from the sharp The effect of sharpening is just the opposite, so this method is called unsharp masking method. [0003] The principle of the classic linear unsharp masking method i...

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Problems solved by technology

[0008] (2) Overshoot phenomenon, because the high-detail area of ​​the image is enhanced more than other areas, the processed image will show obvious traces of artificial processing;

[0009] In the published literature at home and abroad, in order to overcome the shortcomings of the linear unsharp mask method, especially the sensitivity to noise, researchers in the image field have proposed various methods, most of which use nonlinear The filter replaces the linear high-pass filter, and considers the compromise between reducing noise and enhancing details: S.K.Mitra proposes a nonlinear operator based on the Teager algorithm, which can be approximated as a local mean weight high-pass filter; G .Ramponi proposed a cubic unsharp mask method. The essence of this method is to multiply an edge-sensitive square filter operator by the Laplacian operator to only enhance the image details in the local brightness change area. Small noise; Y.H.Lee proposed an operator based on the Laplace method of sequence statistics. The output of this operator is proportional to the difference between the local mean and the local median, which can effectively remove Gaussian white noise; A.Polesel proposed Adaptive unsharp mask method, this method uses an adaptive filter to enhance the details of the image to a greater extent, but hardly enhances the flat area of ​​​​the image, thus reducing the noise in the flat area; however, the above mentioned Although the method reduces the noise compared with the linear unsharp mask method, the noise is still relatively obvious in the flat area, and in order to achieve a better enhancement effect in the detailed area of ​​the image, the high-detail area of ​​​​the image is often enhanced Too large, resulting in overshoot phenomenon

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