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Noise image enhancing method based on non-linear Curvelet diffusion

An image enhancement and non-linear technology, applied in image enhancement, image data processing, instruments, etc., to achieve the effect of noise elimination, pseudo-Gibbs effect reduction, and good noise suppression

Active Publication Date: 2011-06-01
南通丝乡丝绸有限公司 +1
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Problems solved by technology

Although this method has a significant improvement in performance over wavelet transform-based methods, it is only suitable for images with no noise or very little noise.

Method used

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  • Noise image enhancing method based on non-linear Curvelet diffusion
  • Noise image enhancing method based on non-linear Curvelet diffusion
  • Noise image enhancing method based on non-linear Curvelet diffusion

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Embodiment Construction

[0027] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0028] The images used in the present invention are defined in R 2 Grayscale image I with Gaussian noise on 0 , each image corresponds to a matrix, and each pixel in the image corresponds to a corresponding element in the matrix;

[0029] Step 1: For the input noise image I 0 Perform ME-curvelet forward transformation to obtain the ME-curvelet coefficient matrix C in different directions l under different decomposition scales j 0 (j, l), where j=1, 2,..., J, J is the finest decomposition scale, l=1,..., L j , L j is the number of directions at the jth scale;

[0030] Step 2: Press σ=median(|S for the input image HH |) / 0.6745 to estimate the noise standard deviation, where median(·) means to take the median of all coefficients in the matrix, |·| means to take the modulus, S HH is for image I 0 Carrying out the wavelet coefficient matrix of high-frequency s...

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Abstract

The invention relates to a noise image enhancing method based on non-linear Curvelet diffusion, which is characterized in that the blurry edge and positioning problems are avoided occurring in isotropic diffusion by utilizing the heat diffusion in the nature in the isotropic diffusion process based on a PDE (Partial Differential Equation) method. By using the method, the noise removing and the edge maintaining are synchronously realized, but the method is very sensitive to the noise. Aiming at the noise image, the invention provides an enhancing method combining Curvelet (Mirror-extended Curvelet, ME-Curvelet) conversion with non-linear diffusion. By using the method, the edge character of image and the contrast are enhanced while the noise therein is restrained, the fake Gibbs effect can be efficiently reduced, and the image quality is further increased.

Description

technical field [0001] The invention relates to a noise image enhancement method based on nonlinear Curvelet diffusion, in particular to a noise image enhancement method based on nonlinear Curvelet diffusion. Background technique [0002] Image enhancement is a classic problem in image processing. By selectively enhancing the features of interest in the image while attenuating its secondary information, image readability and image interpretation capabilities are enhanced, laying the foundation for subsequent image processing. Traditional image enhancement methods mainly include spatial domain methods such as histogram equalization and unsharp masking, and frequency domain methods that enhance frequency components of interest through Fourier transform. While these methods enhance the contrast of the image, they also amplify the noise, so that the detailed information of the image is overwhelmed by the noise. [0003] Wavelet transform enhancement algorithm is a widely popula...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 李映宁慧君张艳宁
Owner 南通丝乡丝绸有限公司
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