Digital image enhancement method and device based on wavelet restruction and decompose

Abstract

The invention relates to a digital image reinforcing method based on wavelet reconstruction and decomposition and a device thereof, which belongs to the image data processing field. The digital image reinforcing method comprises image pretreatment, decomposition process, transformation process and reconstruction process, and is characterized in that the image decomposition process comprises multi-group integrals and down-sampling process; the transformation process comprises the statistics, analysis and modification processes of mid data information; the reconstruction process comprises multi-group up-sampling, integrals and adding processes; in the transformation process, adaptive histogram equalization technology and noise restraining are adopted. The invention, based on multi-scale analysis, realizes to reinforce the contrast of digital image and prevents noise simultaneously through the wavelet decomposition, transformation and reconstruction and adopting the adaptive histogram equalization technology and noise restraining in transform domain and can be widely used in such fields related to the improvement of digital image quality as medical information systems, industrial nondestructive testing systems and other similar image systems.

Description

technical field [0001] The invention belongs to the field of image data processing or generation, and in particular relates to an enhancement method and device for digital images. Background technique [0002] For an image processing system, the processing flow can be divided into three stages. After the original image is acquired, the first is the image preprocessing stage, the second is the feature extraction stage, and the third is the recognition analysis stage. The image preprocessing stage is particularly important. If this stage is not handled well, the subsequent work cannot be carried out at all. [0003] In practical applications, the original image acquired by our system is not perfect. For example, for the original image acquired by the system, the quality of the image is not high due to noise, illumination, etc., so preprocessing is required to facilitate the extraction of the image we are interested in. Information. Image preprocessing usually includes image ...

AI Technical Summary

Problems solved by technology

[0010] 1. Since the contrast is enhanced by expanding the dynamic range, the image details of small areas are ignored;

[0011] 2. Because the dynamic range is extended to enhance the contrast, there will be edge "ringing" artifacts;

[0012] 3. Since the contrast is enhanced by expanding the dynamic range, the resolution of the image contrast will be very low as a result of the global histogram transformation;

[0013] 4. If the signal noise is distributed in a wide dynamic range, the result of suppressing noise will lose image details;

[0014] 5. If the information of the entire dynamic range of the image is displayed on a display with a limited dynamic range (dynamic range <100:1, CRT), or printed on film (dynamic range <500:1), after subsampling by the display system, Display contrast will be very low;

[0019] Therefore, the existing image processing technology of histogram equalization and histogram matching (regulation) can extend the dynamic range to perform grayscale transformation on low-contrast objects, and cannot highlight such small grayscale differences.

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