Commit 11ef40a3 authored by 袁通's avatar 袁通
Browse files

update LAB6

parent 28efc3c1

Lab5_Frequency_Domain_Filtering/report/frquency_domain_filtering_11810818.fdb_latexmk

+9 −9
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# Fdb version 3

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  "frquency_domain_filtering_11810818.xdv"
 
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Lab6_Image_Restoration/EE326_SUSTech.py

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@@ -7,9 +7,10 @@ from skimage import io, data 
import math

from scipy import interpolate

import matplotlib.pyplot as plt



from numba import njit,prange



# General



def format_image(input_image):

    output_image = input_image

    output_image -= np.min(output_image)
@@ -153,7 +154,7 @@ def centering(size): 
        mul1 *= -1

    return centering_matrix





@njit

def generating_from_spatial_filter(input_filter, P, Q):

    output_filter = np.zeros(P, Q)
@@ -181,6 +182,45 @@ def butterworth_filter(b, a, center, n, sigma): 




# LAB 6

@njit(parallel=True)

def adaptive_filter(input_image, n_size, smax):

    output_image = np.zeros(input_image.shape, dtype=np.uint8)

    m, n = input_image.shape



    for i in prange(m):

        for j in prange(n):

            n_size_2 = n_size



            while True:

                step = (int)((n_size_2 - 1) / 2)

                pixels = np.zeros(n_size_2 * n_size_2)



                for i2 in range(n_size_2):

                    for j2 in range(n_size_2):

                        if i - step + i2 >= 0 \

                                and i - step + i2 < input_image.shape[0] \

                                and j - step + j2 >= 0 \

                                and j - step + j2 < input_image.shape[0]:

                            pixels[j2 * n_size_2 + i2] = input_image[i - step + i2, j - step + j2]



                pixels_sorted = np.sort(pixels)

                med = (int)((n_size_2 * n_size_2-1)/2)

                a1 = pixels_sorted[med] - pixels_sorted[0]

                a2 = pixels_sorted[med] - pixels_sorted[n_size_2 * n_size_2-1]

                if(a1>0 and a2<0):

                    b1 = input_image[i, j] - pixels_sorted[0]

                    b2 = input_image[i, j] - pixels_sorted[n_size_2 * n_size_2-1]

                    if(b1>0 and b2<0):

                        output_image[i, j] = pixels[med]

                    else:

                        output_image[i, j] = pixels_sorted[med]

                    break

                else:

                    if(n_size_2 < smax):

                        n_size_2 += 2

                    else:

                        output_image[i, j] = pixels_sorted[med]

                        break



    return output_image

Lab6_Image_Restoration/Q6_3_1_spectrum.png

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Lab6_Image_Restoration/Q6_3_2.png

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Lab6_Image_Restoration/Q6_3_3.png

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