Image enhancement method and image enhancement system based on second-generation wavelet integer transform

Abstract

The invention relates to an image enhancement method and an image enhancement system based on second-generation wavelet integer transform. The image enhancement method comprises the following steps: S1, performing single-layer decomposition on an original graph to get an original integer low-frequency sub-graph ca; S2, calculating the original integer low-frequency sub-graph ca to get a first integer low-frequency sub-graph ca'; S3, calculating the first integer low-frequency sub-graph ca' to get a second integer low-frequency sub-graph ca''; and S4, reconstructing the second integer low-frequency sub-graph ca'' to get an enhanced new image. According to the invention, through second-generation wavelet integer transform, single-layer decomposition is performed on an image, and the low-frequency sub-graph coefficient is equalized. The image noise is reduced while the image is enhanced effectively, and an ideal image processing effect is achieved.

Description

technical field [0001] The invention relates to an image enhancement technology, which belongs to the field of image processing, in particular to an image enhancement method and system based on second-generation wavelet integer transformation. Background technique [0002] The purpose of image enhancement is to improve the visual effect of the image and provide an intuitive, clear and suitable image for analysis. There are many image enhancement methods, among which histogram equalization is one of the classic and effective image enhancement methods. Although the histogram equalization algorithm has many advantages such as fast operation speed and obvious enhancement effect, it still has the following obvious defects: (1) When the gray dynamic range of the original image is small, the quality is relatively poor, and the histogram distribution is extremely uneven, after The layering of the image after traditional histogram equalization will become very poor; (2) the noise su...

AI Technical Summary

Problems solved by technology

Although the histogram equalization algorithm has many advantages such as fast operation speed and obvious enhancement effect, it still has the following obvious defects: (1) When the gray dynamic range of the original image is small, the quality is relatively poor, and the histogram distribution is extremely uneven, after The layering of the image after traditional histogram equalization will become very poor; (2) the noise superimposed in the original image will be significantly enlarged after being enhanced by the traditional histogram; (3) if the grayscale range in an image is close to 0 When the equalization algorithm is performed, a very narrow dark pixel interval is mapped to the output image, and a bright and diluted image will be obtained as a result, resulting in the basic characteristics of the image such as changes in average brightness and loss of details, affecting the enhanced image. The visual effect of the histogram algorithm is limited

The basic idea is to transform nonlinear problems into linear problem processing. The disadvantage of homomorphic filter enhancement is that a lot of image details will be lost during the enhancement process of noisy images.

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