I see the BBS for some time, also know a lot of people are entangled with the wiener filtering, I now also is to have algorithm, but how C + + programs do not know how to write, only the basis of C language, wish elder people teach me and instruct me, because it's urgent need, do not to come out will be dragged his teammates, I knew that ability not line, please let me through this close, later learn well, sister kneeling for help

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The algorithm is posted

CodePudding user response:

Image degradation:
Image in formation, recording, processing and transmission in the process, because of the imaging systems, recording equipment, transmission medium and the processing method is not perfect, lead to a drop in the quality of image, this phenomenon is called image degradation,
Image restoration:
Is the degradation of image processing, as far as possible the real appearance of recovering the original image,
Image restoration method:
The key is to the degradation of image function to estimate the degradation and noise function, and then can recover operator, restore computation, can recover on airspace, can also recover in frequency domain,
Several noise models:
Gaussian noise Rayleigh noise index of uniform noise salt - and - pepper noise,
The estimated noise commonly used method is:
Estimates the noise from image relatively flat areas in the form of probability density function and parameters, concrete, shear flat area, first calculate the flat area of the histogram, calculating the mean and variance of plain area, and then the parameters of the probability density function is deduced,
Point spread function:
Mainly studied the point spread function of the linear motion blur,
If the ideal image is a point? Sigma (x, y)=1, image acquisition and image is not ideal, the system of real image is not a point, but a group of be extended (spread out) the point of h (x, y),
Several kinds of recovery methods:
Mainly studied the frequency domain inverse filter for image restoration and image restoration recover wiener filtering, inverse filtering recovery method is to ignore the noise, when when the degradation function is smaller, serious noise is amplified, the result is bad; Wiener filtering combining the degradation function and noise statistical characteristics of two aspects of recovery processing, looking for a filter, so that when the recovery image and the minimum mean square error (mse) of the original image, so the wiener filtering is also known as minimum mean square error filter,

















Wiener filtering image restoration algorithm:
From the focal blur image restoration based on wiener filtering is the key to determine the degradation model of h (x, y) and image SNR gamma, rehabilitation concrete implementation steps are as follows:
Two-dimensional FFT is used to calculate from the focal blurred image discrete Fourier transform of G (u, v), and the corresponding amplitude spectrum (u, v) | | G,
U from 0 to M - 1, U from zero to U do double loop for (U, v) | | G corresponding to zero, the first dark ring out all zero corresponding fuzzy radius r, radius of zero radius of fuzzy mathematical expectation is the fuzzy r_0
Respectively using pull operator g (x, y) for differential and two-dimensional fast Fourier transform to calculate differential image autocorrelation, the distribution of autocorrelation has taught coordinates into polar coordinates; According to the estimation of r, calculated S_p and determine the S_ (P=2 gamma), and the parameter h (x, y) is r_0
Take local size for P=Q=2, and image variance calculation, determine the minimum and maximum variance, calculate SNR reciprocal r_0
Using (6) as a filter for a wiener filtering
According to the result of the first filter estimate P_f (u, v) and the estimated variance
The use of (7) for secondary wiener filtering
Image degradation:
Image in formation, recording, processing and transmission in the process, because of the imaging systems, recording equipment, transmission medium and the processing method is not perfect, lead to a drop in the quality of image, this phenomenon is called image degradation,
Image restoration:
Is the degradation of image processing, as far as possible the real appearance of recovering the original image,
Image restoration method:
The key is to the degradation of image function to estimate the degradation and noise function, and then can recover operator, restore computation, can recover on airspace, can also recover in frequency domain,
Several noise models:
Gaussian noise Rayleigh noise index of uniform noise salt - and - pepper noise,
The estimated noise commonly used method is:
Estimates the noise from image relatively flat areas in the form of probability density function and parameters, concrete, shear flat area, first calculate the flat area of the histogram, calculating the mean and variance of plain area, and then the parameters of the probability density function is deduced,
Point spread function:
Mainly studied the point spread function of the linear motion blur,
If the ideal image is a point? Sigma (x, y)=1, image acquisition and image is not ideal, the system of real image is not a point, but a group of be extended (spread out) the point of h (x, y),
Several kinds of recovery methods:
Mainly studied the frequency domain inverse filter for image restoration and image restoration recover wiener filtering, inverse filtering recovery method is to ignore the noise, when when the degradation function is smaller, serious noise is amplified, the result is bad; Wiener filtering combining the degradation function and noise statistical characteristics of two aspects of recovery processing, looking for a filter, so that when the recovery image and the minimum mean square error (mse) of the original image, so the wiener filtering is also known as minimum mean square error filter,

















Wiener filtering image restoration algorithm:
From the focal blur image restoration based on wiener filtering is the key to determine the degradation model of h (x, y) and image SNR gamma, rehabilitation concrete implementation steps are as follows:
Two-dimensional FFT is used to calculate from the focal blurred image discrete Fourier transform of G (u, v), and the corresponding amplitude spectrum (u, v) | | G,
U from 0 to M - 1, U from zero to U do double loop for (U, v) | | G corresponding to zero, the first dark ring out all zero corresponding fuzzy radius r, radius of zero radius of fuzzy mathematical expectation is the fuzzy r_0
Respectively using pull operator g (x, y) for differential and two-dimensional fast Fourier transform to calculate differential image autocorrelation, the distribution of autocorrelation has taught coordinates into polar coordinates; According to the estimation of r, calculated S_p and determine the S_ (P=2 gamma), and the parameter h (x, y) is r_0
Take local size for P=Q=2, and image variance calculation, determine the minimum and maximum variance, calculate SNR reciprocal r_0
Using (6) as a filter for a wiener filtering
According to the result of the first filter estimate P_f (u, v) and the estimated variance
The use of (7) for secondary wiener filtering

CodePudding user response:

Wiener filtering image restoration algorithm:
From the focal blur image restoration based on wiener filtering is the key to determine the degradation model of h (x, y) and image SNR gamma, rehabilitation concrete implementation steps are as follows:
Two-dimensional FFT is used to calculate from the focal blurred image discrete Fourier transform of G (u, v), and the corresponding amplitude spectrum (u, v) | | G,
U from 0 to M - 1, U from zero to U do double loop for (U, v) | | G corresponding to zero, the first dark ring out all zero corresponding fuzzy radius r, radius of zero radius of fuzzy mathematical expectation is the fuzzy r_0
Respectively using pull operator g (x, y) for differential and two-dimensional fast Fourier transform to calculate differential image autocorrelation, the distribution of autocorrelation has taught coordinates into polar coordinates; According to the estimation of r, calculated S_p and determine the S_ (P=2 gamma), and the parameter h (x, y) is r_0
Take local size for P=Q=2, and image variance calculation, determine the minimum and maximum variance, calculate SNR reciprocal r_0
Using (6) as a filter for a wiener filtering
According to the result of the first filter estimate P_f (u, v) and the estimated variance
The use of (7) for secondary wiener filtering