Turbulent image denoising method

Abstract

The invention discloses a turbulent image denoising method, comprising the following steps: performing single-layer two-dimensional discrete wavelet transform on the noisy turbulent image; extracting high-frequency coefficients and performing fast discrete Curvelet transform on the noisy turbulent image; The threshold T is estimated by the Yeesian criterion, and the adaptive selection method of the threshold is improved to obtain the optimal threshold and obtain a denoised turbulent image. The present invention provides a turbulent image denoising method, which can well protect image detail information, suppress boundary artifacts, and significantly improve visual effects. At the same time, the invention achieves higher peak signal-to-noise ratio and lower mean square error, and effectively removes the noise of the turbulence degraded image.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a turbulent image denoising method. Background technique [0002] In recent years, scholars at home and abroad have proposed many air target denoising algorithms affected by atmospheric turbulence, and proposed a turbulent image denoising method based on wavelet threshold, which is based on the general threshold shrinkage method to achieve turbulent image denoising. The disadvantage is that the edge is too smooth and the convergence speed of the algorithm is slow; an adaptive field threshold denoising method (Denoising Wavelet Threshold based on NABayesShrink method, DWT-NABayesShrink) is proposed, which is based on wavelet coefficient features combined with a generalized Gaussian model, Realize adaptive neighborhood threshold denoising. The advantage of this method is that it can retain some image details, but the algorithm has a large amount of calculation and slow conve...

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Problems solved by technology

[0002] In recent years, scholars at home and abroad have proposed many air target denoising algorithms affected by atmospheric turbulence, and proposed a turbulent image denoising method based on wavelet threshold, which is based on the general threshold shrinkage method to achieve turbulent image denoising. The disadvantage is that the edge is too smooth and the convergence speed of the algorithm is slow; an adaptive field threshold denoising method (Denoising Wavelet Threshold based on NABayesShrink method, DWT-NABayesShrink) is proposed, which is based on wavelet coefficient features combined with a generalized Gaussian model, Realize adaptive neighborhood threshold denoising. The advantage of this method is that it can retain some image details, but the algorithm has a large amount of calculation and slow convergence; a nonlinear image denoising method based on discrete wavelet transform (Undecimated Discrete Wavelet Transform, UDWT ), this method uses non-orthogonal base wavelet transform with non-sampling and displacement invariance, which is different from the orthogonal wavelet transform proposed by Donoho et al. The advantage of this method is that it can significantly reduce image noise and protect image edge information well, but the image Insufficient detail information

[0003] Affected by the structure of the imaging system and atmospheric turbulence and other factors, the observation image contains a lot of noise, which will cause serious distortion of the target image and hinder the positioning, detection and tracking of air targets.

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