Multifunctional image processing method based on wavelet transform

Abstract

The invention provides a multifunctional image processing method based on wavelet transform. The multifunctional image processing method comprises the following steps: step 1, reading an original image; 2, decomposing the original image into a high-frequency part and a low-frequency part by wavelet transform; 3, for the high-frequency part of the image, performing threshold quantization processingon all high-frequency coefficients, and then performing median filtering to complete compression of the high-frequency part and image enhancement; 4, for the low-frequency part of the image, enhancing a low-frequency coefficient by adopting an improved function; and step 5, reconstructing the processed high-frequency part and the processed low-frequency part by using wavelet inverse transformation to obtain a reconstructed image. According to the method, wavelet transformation is adopted to process the image, so that the entropy after signal transformation is reduced, the non-stationarity ofthe signal can be well described, and feature extraction and protection are facilitated. According to the method, wavelet transform is adopted, so that denoising is more facilitated in a wavelet domain than in a time domain, and different wavelet functions can be selected according to different application requirements to obtain an optimal processing effect.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a multifunctional image processing method based on wavelet transform. Background technique [0002] Like the Fourier transform (FT), the wavelet transform (WT) also belongs to the category of mathematical transformation. It uses a cluster of wavelet function systems to represent or approximate a certain signal, so the wavelet function ψ( x) and scaling function To express the relationship between them, the specific relationship is as follows: [0003] [0004] where x refers to the signal, j 0 For any starting scale, is the scale function, which corresponds to the approximate subband in the two-dimensional wavelet transform of the image, c j0 (k) is a scale coefficient, which is just a coefficient in a scale function. wavelet coefficient with d j (k) to represent, ψ j,k (x) represents the wavelet function, which usually forms a set of orthogonal bases, corresponding to...

AI Technical Summary

Problems solved by technology

[0014] If the traditional wavelet transform image enhancement algorithm wants to effectively highlight the detailed information of the image, it needs to use the enhanced wavelet coefficient while maintaining the image brightness. The most common problem encountered by the traditional wavelet transform image enhancement algorithm in the process of processing is that it will enlarge The noise in the image, therefore, how to effectively suppress the noise needs to be solved urgently

[0015] 2. Single image processing technology based on wavelet transform

[0016] During the process of image acquisition and transmission, it will be affected by the imaging environment, imaging equipment and transmission system, making the final received image contain noise, and the details are difficult to identify, which greatly hinders the accuracy of subsequent image processing

After the image is preprocessed by a single image processing technology, it often cannot effectively solve the problem, which is not conducive to the smooth analysis and processing of the subsequent image.

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