Turbulent image de-noising method

Abstract

The invention discloses a turbulent image de-noising method, which comprises the following steps: carrying out single-layer two-dimensional discrete wavelet transform on a noised turbulent image; extracting a high-frequency coefficient and carrying out Fast discrete Curvelet transform on the noised turbulent image; and according to Bayesian criterion estimation threshold T, improving an adaptive selection method of the threshold, obtaining an optimum threshold and obtaining a de-noised turbulent image. The turbulent image de-noising method can protect image detail information very well and suppress boundary artifacts, and thus visual effect is improved obviously; and meanwhile, the method obtains higher peak signal-to-noise ratio and smaller mean square error, and removes the noise in the turbulence-degraded images.

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; a threshold denoising method (Denoising Wavelet Threshold based on NABayesShrink method, DWT-NABayesShrink) in the adaptive field is proposed, which is based on the wavelet coefficient feature and combined with the 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...

AI Technical Summary

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; a threshold denoising method (Denoising Wavelet Threshold based on NABayesShrink method, DWT-NABayesShrink) in the adaptive field is proposed, which is based on the wavelet coefficient feature and combined with the 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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