Construction method for gray-level information content histogram

Abstract

The invention relates to a construction method for a gray-level information content histogram, comprising the following steps: acquiring the scale space expressions of an input image under different scales, and folding the input image with Gausses of different scale factors to obtain the multiscale space expression of the input image, namely a Gaussian pyramid image; acquiring the DoG pyramid multiscale expression of the input image, detecting a scale space extreme point on the basis, and storing the corresponding scale information; revising the statistical weight of each pixel, assigning 1 for the increment of the statical weight which is in a local high information area and is detected by the scale space, assigning 0 for the increment of the statical weight outside the area, and adopting a progressive scanning mode to revise the statistical weights of all the pixels in the local high information area; and respectively accumulating the statistical weights of the gray levels of the pixels, thus obtaining the measured result of the information content born by each gray level, namely the gray-level information content histogram. The invention comprehensively considers the quantity and space position distribution, so that the statistical result can better reflect the role of each gray level in describing information.

Description

technical field [0001] The invention relates to a method for processing image gray scale information volume. In particular, it relates to a method for constructing a histogram of gray-level information amount that accurately measures the information amount of each gray-level (or each color level of a certain color component map in a color image) in a gray-scale image. Background technique [0002] With the development of high-speed computers and large-scale integrated circuits, digital image processing technology has made a series of gratifying breakthroughs and progress, and its results are widely used in many fields such as biomedical engineering, industrial manufacturing, space exploration, public security, culture and art, etc. . However, some problems are gradually exposed in the process of popularization and application. Since the image records scene information in the form of a pixel array, it is obvious that the amount of data is large, and the huge amount of data ...

AI Technical Summary

Problems solved by technology

[0004] However, the traditional histogram data expression method also has serious defects: the histogram data only reflects the number (or frequency) of occurrences of different gray levels or color levels in the image, and the information of its location is completely lost in the statistical process. The statistical results cannot objectively reflect the role of each gray level in describing the image information, that is, there is a problem of inconsistency between the gray level statistics and the amount of information, which will inevitably affect the performance of some image analysis algorithms

However, due to the inconsistency between the gray level quantity statistics and the amount of information, it is difficult to guarantee the limited stretching of the gray level that mainly describes the information, which greatly affects the performance and robustness of the histogram-based image enhancement algorithm.

[0005] Moreover, the statistical weight of each pixel in the traditional histogram is the same (both 1), that is, the default importance of each pixel position is the same, which is obviously unreasonable

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