Image nonlinearity enhancement method based on histogram subsection transformation

Abstract

The invention discloses an image nonlinearity enhancement method based on histogram subsection transformation. The method comprises the following steps of: S1, calculating probability density distribution of image gray levels; S2, calculating accumulated probability density functions of the gray levels; S3, dividing the gray levels of the images into three parts, wherein the first part of gray level range is between 0 and 85, the second part of gray level range is between 86 and 170, and the third part of gray level range is between 171 and 255; respectively calculating gray level transformation functions of the three parts of gray levels according to the accumulated probability density functions, namely proposing sectional gray level transformation functions; and S4, transforming the gray levels of the original images by using the sectional gray level transformation functions to acquire the distribution of new gray levels. By the method, the definition of the image is further improved, the excessive enhancement of the image is avoided, and the image vision effect meets the requirement of an eye vision system.

Description

technical field [0001] The invention relates to image processing technology, in particular to an image non-linear enhancement method based on histogram subsection transformation. Background technique [0002] In multi-spectral information fusion, the infrared sensor is imaged through the thermal radiation of the target scene, and the detail information is not rich and the contrast is low; the visible light sensor is imaged through the reflection of the target scene, the detail information is rich, and the contrast is easily affected by the light. The fusion of infrared and visible light images can make full use of the complementarity of their information and improve the detection ability of the detection system. If the image contrast is low, it will affect the multi-spectral fusion effect. Therefore, low-contrast images need to be enhanced before multispectral fusion. [0003] At present, the most typical spatial domain enhancement algorithm is the histogram equalization a...

AI Technical Summary

Problems solved by technology

[0009] The essence of the method of histogram equalization to enhance the image is to reduce the gray level in exchange for an increase in contrast. If the merged gray level constitutes important details, the image enhanced by the histogram equalization algorithm will lose detail information, resulting in The contrast of the image is over-enhanced, which makes the visual effect of the processed image stiff and not soft enough, and sometimes even causes the deterioration of the image quality, which cannot meet the needs of human visual characteristics.

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