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Lifting wavelet image de-noising method based on neighborhood windowing

An image and neighborhood technology, applied in the field of image processing, can solve the problems of large storage space consumption and large amount of calculation, and achieve the effects of fast processing speed, high peak signal-to-noise ratio, and good image denoising effect

Inactive Publication Date: 2011-05-04
TIANJIN UNIV
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Problems solved by technology

[0003] Classical wavelet transform is a time-frequency domain analysis method. Like Fourier transform, although there is a fast calculation method, it still has the disadvantages of large amount of calculation, large storage space consumption and floating point calculation.

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  • Lifting wavelet image de-noising method based on neighborhood windowing
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  • Lifting wavelet image de-noising method based on neighborhood windowing

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Embodiment Construction

[0019] The present invention will be described in further detail below by means of the accompanying drawings and examples.

[0020] 1. Using lifting wavelet transform for image transformation

[0021] The second-generation wavelet transform process based on the lifting method consists of three steps: split, predict and update ( image 3 shown).

[0022] splitting is to divide the input signal s i Divided into two smaller subsets, the signal can be divided arbitrarily, but to make the divided data have greater correlation, the simplest decomposition method is to divide the input signal s i According to the parity, it is divided into 2 groups, that is, the sub-signal s composed of its even-numbered samples e =s 2k (k∈Z), and the sub-signal s composed of its odd-numbered samples o =s 2k+1 (k∈Z), the decomposition process is expressed as Split(s i )=(s e ,s o )

[0023] Prediction is to compute the detail signal by approximating the signal. On the basis of maintaining th...

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Abstract

The invention belongs to the technical field of image processing and relates to a lifting wavelet image de-noising method based on neighborhood windowing, comprising the following steps: processing a noisy image f(x, y) by single-scaling lifting wavelet transformation, respectively obtaining four sub-band coefficients: a low-frequency coefficient A, a horizontal detail coefficient H, a vertical detail coefficient V and a diagonal detail coefficient D; keeping the low-frequency coefficient A, performing mean filtering to the horizontal detail coefficient H, the vertical detail coefficient V and the diagonal detail coefficient D by respectively adopting a vertical linear filtering template, a horizontal linear filtering template and a diagonal direction filtering template, and the results being H', V' and D' after the filtering, reconstructing the A and the de-noised high-frequency sub bands to obtain the de-noised image. The de-noising method provided by the invention utilizes the characteristic of rapid processing speed of the lifting wavelet for achieving a higher peak signal-noise ratio and having better image de-noising effect.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a lifting wavelet image denoising method based on neighborhood windowing. Background technique [0002] Among the current image denoising methods, mean filtering is a commonly used image filtering and denoising method. This method is simple in operation and has good denoising ability for Gaussian noise. However, the mean filter will also cause damage and loss to the high-frequency detail components of the image while eliminating the noise, making the image blurred. In order to solve the image blurring problem in the mean filtering algorithm, many improved algorithms have also appeared, such as K neighbor average method, gradient reciprocal weighted smoothing method, maximum uniformity smoothing method, small slope model smoothing method, adaptive weighted smoothing method, etc. , but these improved mean filtering algorithms generally only have better denoisi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
Inventor 宫霄霖毛瑞全刘开华
Owner TIANJIN UNIV
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