Endoscope image enhancement method based on histogram equalization and improved unsharpened mask

Abstract

The invention discloses an endoscope image enhancement method based on histogram equalization and improved anti-sharpening mask, which relates to the technical field of image enhancement, and comprises the following steps: acquiring an endoscope image; carrying out local brightness adjustment and saturation correction on the endoscope image by adopting an HSV color model; converting the image into a YCbCr space, and performing low-frequency background enhancement on a Y component by adopting a contrast-limited adaptive histogram equalization method; and carrying out canny texture detection, and carrying out high-frequency detail enhancement by using the improved nonlinear unsharp mask to obtain an improved endoscope enhanced image. According to the image enhancement method, a histogram equalization method, canny edge detection and an improved nonlinear anti-sharpening mask are innovatively combined, so that the low-frequency component of background intensity is effectively balanced, contour details are enhanced, the original color of the image is kept while the image contrast is naturally enhanced and detail information such as blood vessels is revealed, and the noise is reduced.

Description

technical field [0001] The invention relates to the technical field of image enhancement, in particular to an endoscope image enhancement method based on histogram equalization and improved unsharp mask. Background technique [0002] Due to the limitation of lighting conditions and the complexity of the surgical environment, the images collected by the endoscope are often not clear enough and have low contrast, which affects the doctor's diagnosis. Therefore, it is necessary to enhance the contrast of endoscopic images to provide intuitive and clear information to help doctors interpret the images more easily. [0003] Modern image enhancement techniques can be divided into three categories, namely Retinex-based methods, Unsharp Mask (UM)-based methods, and Non-linear Intensity Transform (NIT)-based methods. Retinex-based methods usually produce halo effects, and the algorithm is relatively complex and time-consuming; UM-based detail enhancement methods deal with high-frequ...

AI Technical Summary

Problems solved by technology

At present, there are methods such as single-scale Retinex and multi-scale Retinex, but these methods have certain defects: 1) Halo phenomenon is prone to appear in the strong light shadow transition area, mainly because the Gaussian operator cannot estimate the light well in the transition area due to

2) The processing of brighter images is not good, the main reason is that logarithmic processing compresses the display range of bright areas, resulting in weakening of its details

Since the noise is also a high-frequency image, it will amplify the image noise or cause the edge part to overshoot while enhancing the image, resulting in a decrease in image quality

[0010] Therefore, in view of the noise amplification, artifacts and local color distortion problems existing in endoscopic images in the prior art, how to enhance the contrast of endoscopic images and help doctors interpret images more easily is an urgent need for those skilled in the art to solve question

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