Method and system for acquiring image gamma curve and enhancing image contrast

Abstract

The invention discloses a method and system for acquiring an image gamma curve and enhancing image contrast. The method for acquiring the image gamma curve includes the following steps that a histogram of an image is acquired; first smoothing is performed on the histogram; threshold segmentation is performed on the histogram subjected to the first smoothing so that a dark area histogram and a bright area histogram can be obtained; the dark area histogram and the bright area histogram are normalized; the gamma curve is acquired according to the normalized histograms. The gamma curve acquired through the steps has the advantage of being good in smoothness, and when the gamma curve is used for correcting image contrast, the image will not be locally too dark or bright. Thus, the problem that an image is locally too dark or bright happens to a method for enhancing image contrast in the prior art is solved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method and system for obtaining an image gamma curve and enhancing image contrast. Background technique [0002] In general ISP (Image Signal Processing, that is, image signal processing), a gamma (gamma) curve can be used to set the contrast of an image. Generally, a fixed gamma curve is used to set the mapping method of the three color channels. However, using a single gamma curve is obviously not applicable to all possible scenarios. Using a higher contrast gamma curve may make normal pictures It becomes darker, the details of the dark part are lost, and the use of a gamma curve with a weaker contrast may make the magnified distant image unclear and transparent. Therefore, to solve the problem of optimizing the contrast of the adjusted image in different scenarios, an alternative approach is to make the gamma curve adaptive. [0003] At present, there are many way...

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Problems solved by technology

[0004] Document 1 (Journal of Intelligent Manufacturing, Springer US, Volume 25, Issue 2, pp:303-318, 2012.) mainly uses the method of three-segment fitting of the histogram integral curve to obtain the histogram segmentation threshold, and then for each segment Re-map, and finally generate a gamma curve through the method of histogram equalization. This method can enhance dark and bright areas in pictures with strong contrast between light and dark. However, this method cannot manually adjust the contrast and cannot preserve local details. Control, sometimes partial dead black or overexposure of bright places, large amount of calculation and other shortcomings

[0005] Document 2 (Communications in Computer and Information Science, Gandhigram, India, Volume 140, pp 129-136, 2011.) mainly uses simple histogram correction, equalization and sharpening methods to obtain the gamma curve, which can make the image details more accurate. More, the overall contrast is increased, but the histogram correction parameters in this method are not adaptive in different environments, and the overall equalization of the histogram will produce the defects of local over-darkness and over-exposure, and local noise enhancement

[0006] Therefore, many image contrast enhancement methods in the prior art have the problem that the image is locally too dark or too bright, and there are also problems such as non-adaptive parameters and loss of image details.

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