import cv2 import numpy as np import matplotlib.pyplot as plt import random import numpy as np import matplotlib.pyplot as plt #给图像添加椒盐噪声 def add_salt_and_pepper_noise(src, percentage=0.1): #NoiseImg = src #使用此语句传递的是地址，程序会出错 NoiseImg = src.copy() #在此要使用copy函数，否则src和主程序中的img都会跟着改变 NoiseNum = int(percentage * src.shape[0] * src.shape[1]) for i in range(NoiseNum): randX = np.random.randint(0, src.shape[0] - 1) #产生[0, src.shape[0] - 1]之间随机整数 randY = np.random.randint(0, src.shape[1] - 1) if random.randint(0, 1) == 0: NoiseImg[randX, randY] = 0 else: NoiseImg[randX, randY] = 255 return NoiseImg # 添加高斯噪声 def add_gaussian_noise(src,mu,sigma): NoiseImg=src.copy() NoiseImg=NoiseImg/NoiseImg.max() rows=NoiseImg.shape[0] cols=NoiseImg.shape[1] for i in range(rows): for j in range(cols): NoiseImg[i,j]=NoiseImg[i,j]+np.random.normal(mu,sigma) if NoiseImg[i,j]< 0: NoiseImg[i,j]=0 elif NoiseImg[i,j]>1: NoiseImg[i,j]=1 NoiseImg=np.uint8(NoiseImg*255) return NoiseImg # 添加Rayleigh噪声 # def add_rayleigh_noise(src,scale): # NoiseImg=src.copy() # NoiseImg=NoiseImg/NoiseImg.max() # rows=NoiseImg.shape[0] # cols=NoiseImg.shape[1] # for i in range(rows): # for j in range(cols): # NoiseImg[i,j]=NoiseImg[i,j]+np.random.rayleigh(scale) # if NoiseImg[i,j]< 0: # NoiseImg[i,j]=0 # elif NoiseImg[i,j]>1: # NoiseImg[i,j]=1 # NoiseImg=np.uint8(NoiseImg*255) # return NoiseImg def add_mean_noise(img, a=50, b=150, percentage=0.5): '''添加均匀分布噪声''' NoiseImg = img.copy() NoiseNum = int(percentage * img.shape[0] * img.shape[1]) for i in range(NoiseNum): randX = random.randint(0, img.shape[0] - 1) randY = random.randint(0, img.shape[1] - 1) NoiseImg[randX, randY] = np.random.randint(a, b) return NoiseImg def add_rayleigh_noise(img, a=50, b=150, percentage=1): '''添加瑞利噪声''' NoiseImg = img.copy() NoiseNum = int(percentage * img.shape[0] * img.shape[1]) for i in range(NoiseNum): randX = random.randint(0, img.shape[0] - 1) randY = random.randint(0, img.shape[1] - 1) NoiseImg[randX, randY] = a + (-b * np.log(1 - np.random.rand())) ** 0.5 return NoiseImg def add_erlang_noise(img, a=50, b=0.1, percentage=1): '''添加伽马噪声''' NoiseImg = img.copy() NoiseNum = int(percentage * img.shape[0] * img.shape[1]) for i in range(NoiseNum): randX = random.randint(0, img.shape[0] - 1) randY = random.randint(0, img.shape[1] - 1) NoiseImg[randX, randY] = a- np.log(1 - np.random.rand()) / b return NoiseImg '''openCV的五种滤波器 3种线性滤波： 方框滤波boxFilter(g_srcImage, g_dstImage1, -1, Size(g_nBoxFilterValue+1,g_nBoxFilterValue+1)); 均值滤波blur(g_srcImage,g_dstImage2,Size(g_nMeanBlurValue+1,g_nMeanBlurValue+1),Point(-1,-1)); 高斯滤波 GaussianBlur(g_srcImage, g_dstImage3, Size(g_nGaussianBlurValue*2+1,g_nGaussianBlurValue*2+1), 0, 0); 2种非线性滤波： 中值滤波medianBlur(g_srcImage, g_dstImage4, g_nMedianBlurValue*2+1); 双边滤波bilateralFilter(g_srcImage, g_dstImage5, g_nBilateralFilterValue, g_nBilateralFilterValue*2, g_nBilateralFilterValue/2); ''' def mean_filter(input_image, filter_size, title=''): '''均值滤波''' input_image_cp = np.copy(input_image) filter_template = np.ones((filter_size, filter_size)) pad_num = int((filter_size - 1) / 2) input_image_cp = np.pad(input_image_cp, (pad_num, pad_num), mode="constant", constant_values=0) m, n = input_image_cp.shape output_image = np.copy(input_image_cp) for i in range(pad_num, m - pad_num): for j in range(pad_num, n - pad_num): output_image[i, j] = np.mean( filter_template * input_image_cp[i - pad_num:i + pad_num + 1, j - pad_num:j + pad_num + 1]) output_image = output_image[pad_num:m - pad_num, pad_num:n - pad_num] return output_image def geometric_filter(input_image, filter_size, title=''): '''几何滤波''' input_image_cp = np.copy(input_image) filter_template = np.ones((filter_size, filter_size)) pad_num = int((filter_size - 1) / 2) input_image_cp = np.pad(input_image_cp, (pad_num, pad_num), mode="constant", constant_values=0) m, n = input_image_cp.shape output_image = np.copy(input_image_cp) for i in range(pad_num, m - pad_num): for j in range(pad_num, n - pad_num): output_image[i, j] = \ np.cumprod(filter_template * input_image_cp[i - pad_num:i + pad_num + 1, j - pad_num:j + pad_num + 1])[ -1] ** (1 / (filter_size * filter_size)) output_image = output_image[pad_num:m - pad_num, pad_num:n - pad_num] return output_image def median_filter(pic, filt_size, title=''): '''中值滤波''' pic = np.copy(pic) shape_x, shape_y = pic.shape pad_num = (filt_size - 1) // 2 new_pic = np.zeros([shape_x + 2 * pad_num, shape_y + 2 * pad_num]) new_pic[pad_num: pad_num + shape_x, pad_num: pad_num + shape_y] = pic for i in range(0, shape_x): for j in range(0, shape_y): mean = sorted(new_pic[i + pad_num - 1: i + filt_size, j: j + filt_size].reshape(-1))[filt_size**2 // 2] pic[i][j] = mean return pic def max_filter(pic, filt_size, title=''): '''最大值滤波''' pic = np.copy(pic) shape_x, shape_y = pic.shape pad_num = (filt_size - 1) // 2 new_pic = np.zeros([shape_x + 2 * pad_num, shape_y + 2 * pad_num]) new_pic[pad_num: pad_num + shape_x, pad_num: pad_num + shape_y] = pic for i in range(0, shape_x): for j in range(0, shape_y): mean = np.max(new_pic[i: i + filt_size, j: j + filt_size]) pic[i][j] = mean # plt.imshow(pic, cmap='gray') # plt.title(f'max_filt-{title}') # plt.show() return pic def min_filter(pic, filt_size, title=''): '''最小值滤波''' pic = np.copy(pic) shape_x, shape_y = pic.shape pad_num = (filt_size - 1) // 2 new_pic = np.zeros([shape_x + 2 * pad_num, shape_y + 2 * pad_num]) new_pic[pad_num: pad_num + shape_x, pad_num: pad_num + shape_y] = pic for i in range(0, shape_x): for j in range(0, shape_y): mean = np.min(new_pic[i: i + filt_size, j: j + filt_size]) pic[i][j] = mean # plt.imshow(pic, cmap='gray') # plt.title(f'min_filt-{title}') # plt.show() return pic def harmonic_filter(input_image, filter_size, title=''): '''谐波滤波''' input_image_cp = np.copy(input_image) filter_template = np.ones((filter_size, filter_size)) pad_num = int((filter_size - 1) / 2) input_image_cp = np.pad(input_image_cp, (pad_num, pad_num), mode="constant", constant_values=0) m, n = input_image_cp.shape output_image = np.copy(input_image_cp) for i in range(pad_num, m - pad_num): for j in range(pad_num, n - pad_num): output_image[i, j] = 1 / np.mean( 1 / (filter_template * input_image_cp[i - pad_num:i + pad_num + 1, j - pad_num:j + pad_num + 1])) output_image = output_image[pad_num:m - pad_num, pad_num:n - pad_num] # plt.imshow(output_image, cmap='gray') # plt.title(f'harmonic_filter{title}') # plt.show() return output_image def contra_harmonic_filter(input_image, filter_size, Q, title=''): '''逆谐波滤波''' input_image_cp = np.copy(input_image) filter_template = np.ones((filter_size, filter_size)) pad_num = int((filter_size - 1) / 2) input_image_cp = np.pad(input_image_cp, (pad_num, pad_num), mode="constant", constant_values=0) m, n = input_image_cp.shape output_image = np.copy(input_image_cp) for i in range(pad_num, m - pad_num): for j in range(pad_num, n - pad_num): output_image[i, j] = np.sum( np.power((filter_template * input_image_cp[i - pad_num:i + pad_num + 1, j - pad_num:j + pad_num + 1]) + 0.0001, Q + 1)) / np.sum( np.power((filter_template * input_image_cp[i - pad_num:i + pad_num + 1, j - pad_num:j + pad_num + 1]) + 0.0001, Q)) output_image = output_image[pad_num:m - pad_num, pad_num:n - pad_num] # plt.imshow(output_image, cmap='gray') # plt.title(f'contra_harmonic_filter-{title}') # plt.show() return output_image def medpoint_filt(pic, filt_size, title=''): '''中点滤波''' pic = np.copy(pic) shape_x, shape_y = pic.shape pad_num = (filt_size - 1) // 2 new_pic = np.zeros([shape_x + 2 * pad_num, shape_y + 2 * pad_num]) new_pic[pad_num: pad_num + shape_x, pad_num: pad_num + shape_y] = pic for i in range(0, shape_x): for j in range(0, shape_y): mean = (np.max(new_pic[i: i + filt_size, j: j + filt_size]) + np.min( new_pic[i: i + filt_size, j: j + filt_size])) / 2. pic[i][j] = mean # plt.imshow(pic, cmap='gray') # plt.title(f'mepoint_filt-{title}') # plt.show() return pic def alpha_filter(input_image, filter_size, d, title=''): '''修改的alpha滤波''' input_image_cp = np.copy(input_image)#深拷贝 filter_template = np.ones((filter_size, filter_size))#创建滤波核 pad_num = int((filter_size - 1) / 2)#给图像周围添加元素值 input_image_cp = np.pad(input_image_cp, (pad_num, pad_num), mode="constant", constant_values=0) m, n = input_image_cp.shape output_image = np.copy(input_image_cp) for i in range(pad_num, m - pad_num): for j in range(pad_num, n - pad_num): output_image[i, j] = np.sum(np.sort((filter_template * input_image_cp[i - pad_num:i + pad_num + 1, j - pad_num:j + pad_num + 1]).reshape(1, -1) )[0][d // 2:- (d // 2)] ) / (filter_size ** 2 - d) output_image = output_image[pad_num:m - pad_num, pad_num:n - pad_num] # plt.imshow(output_image, cmap='gray') # plt.title(f'alpha_filter-{title}') # plt.show() return output_image def adaptive_median_filter(pic, max_size): pic = np.copy(pic) shape_x, shape_y = pic.shape pad_num = (max_size - 1) // 2 new_pic = np.zeros([shape_x + 2 * pad_num, shape_y + 2 * pad_num]) new_pic[pad_num: pad_num + shape_x, pad_num: pad_num + shape_y] = pic for i in range(0, shape_x): for j in range(0, shape_y): temp_pad = 1 while True: temp = new_pic[i + pad_num - temp_pad: i + pad_num + temp_pad + 1, j + pad_num - temp_pad: j + pad_num + temp_pad + 1].reshape(-1) min_ = np.min(temp) max_ = np.max(temp) med_ = sorted(temp)[((temp_pad * 2 + 1) ** 2 - 1) // 2] a1 = med_ - min_ a2 = med_ - max_ if a1 > 0 and a2 < 0: b1 = new_pic[i + 1, j + 1] - min_ b2 = new_pic[i + 1, j + 1] - max_ if b1 > 0 and b2 < 0: pic[i][j] = new_pic[i + 1, j + 1] break else: pic[i][j] = med_ break else: temp_pad += 1 if temp_pad * 2 + 1 > max_size: pic[i][j] = med_ break return pic def cross_plot(func, img, sizes, name=''): plt.figure(figsize=[15, 5]) imgs = [] for i in range(len(sizes)): plt.subplot(131 + i) result = func(img, sizes[i]) imgs.append(result) plt.imshow(result, cmap='gray') plt.title(f'{name}-filtsize:{sizes[i]}') plt.savefig(f'img_/{name}') plt.show() return imgs def cross_contra_plot(func, img, sizes, name=''): plt.figure(figsize=[15, 5]) imgs = [] for i in range(len(sizes)): plt.subplot(131 + i) result = func(img, sizes[i], 1.5) imgs.append(result) plt.imshow(result, cmap='gray') plt.title(f'{name}-filtsize:{sizes[i]}') plt.savefig(f'img_/{name}') plt.show() return imgs def cross_alpha_plot(func, img, ds, name=''): plt.figure(figsize=[15, 5]) imgs = [] for i in range(len(ds)): plt.subplot(131 + i) result = func(img, 5, ds[i]) imgs.append(result) plt.imshow(result, cmap='gray') plt.title(f'{name}-d:{ds[i]}') plt.savefig(f'img_/{name}') plt.show() return imgs def cross_autofit_meanplot(func, img, maxsizes, name=''): plt.figure(figsize=[15, 5]) imgs = [] for i in range(len(maxsizes)): plt.subplot(131 + i) result = func(img, maxsizes[i]) imgs.append(result) plt.imshow(result, cmap='gray') plt.title(f'{name}-max:{maxsizes[i]}') plt.savefig(f'img_/{name}') plt.show() return imgs def count_dB(img_a, img_b): '''计算信噪比 单位dB img_a:原图 img_b:滤波之后的图像 ''' img_c = img_a - img_b img_c = img_c.reshape(-1) var_a = np.sum(img_a ** 2) var_c = np.sum(img_c ** 2) snr = var_a / var_c return np.log10(snr) * 10 def count_mse(img_a, img_b): '''计算均方差 img_a:原图 img_b:滤波之后的图像 ''' img_ = img_a - img_b result = np.mean(img_ ** 2) * (1 / img_.shape[0]) * (1 / img_.shape[1]) return result def count_feek_dB(img_a, img_b): '''峰值信噪比 img_a:原图 img_b:滤波之后的图像 ''' mse_ = count_mse(img_a, img_b) return 10 * np.log10(255 ** 2 / mse_) def plot_dB(xs, img_, name, func=count_dB): dBs = [] for img__ in img_: dB = func(img, img__) dBs.append(dB) plt.plot(xs, dBs, '-*') plt.title(name) temp = '' if func == count_mse: temp = 'mse' elif func == count_feek_dB: temp = 'pnsr' elif func == count_dB: temp = 'nsr' plt.savefig(f'img_/{temp}-{name}') plt.show() if __name__=='__main__': # 原图像 img = cv2.imread(r'.\img\lenna.png', 0) # 噪声图像 '''per = .006 gassi_img = add_gaussian_noise(img, var=40, percentage=per) # 高斯噪声 salt_pepper_img = add_salt_and_pepper_noise(img, per) # 椒盐噪声 mean_img = add_mean_noise(img, 0, 252, percentage=per) # 均值噪声 rayleigh_img = add_rayleigh_noise(img, 0, 5000, percentage=per) # 瑞利噪声 erlang_img = add_erlang_noise(img, 0.03, percentage=per) # 伽马噪声 plt.subplot(231) plt.imshow(img, cmap='gray') plt.savefig('./img_/Lenna.png') plt.subplot(232) plt.imshow(gassi_img, cmap='gray') plt.savefig('./img_/gassi_img.png') plt.subplot(233) plt.imshow(salt_pepper_img, cmap='gray') plt.savefig('./img_/salt_pepper_img.png') plt.subplot(234) plt.imshow(rayleigh_img, cmap='gray') plt.savefig('./img_/rayleigh_img.png') plt.subplot(235) plt.imshow(mean_img, cmap='gray') plt.savefig('./img_/mean_img.png') plt.subplot(236) plt.imshow(erlang_img, cmap='gray') plt.savefig('./img_/erlang_img.png') plt.show()''' salt_result_lst = [] papper_result_lst = [] Qs = np.linspace(-3, 3, 13) for metric in zip([count_dB, count_feek_dB, count_mse], ['dB', 'feek_dB', 'mse']): for i in Qs: result_1 = contra_harmonic_filter(salt_img, 3, i) salt_result_lst.append(metric[0](img, result_1)) plt.show() result_2 = contra_harmonic_filter(papper_img, 3, i) papper_result_lst.append(metric[0](img, result_2)) plt.plot(Qs, salt_result_lst, '-*', label='salt') plt.plot(Qs, papper_result_lst, '-*', label='papper') plt.legend() plt.title('{}-{}'.format(metric[1], i)) plt.show() salt_result_lst = [] papper_result_lst = [] # 均值滤波处理 mean_gass_img = cross_plot(mean_filter, gassi_img, [3, 5, 7], 'mean-gassi') mean_salt_pepper_img = cross_plot(mean_filter, salt_pepper_img, [3, 5, 7], 'mean-salt_pepper') mean_mean_img = cross_plot(mean_filter, mean_img, [3, 5, 7], 'mean-mean') mean_rayleigh_img = cross_plot(mean_filter, rayleigh_img, [3, 5, 7], 'mean-rayleigh') mean_erlang_img = cross_plot(mean_filter, erlang_img, [3, 5, 7], 'mean-erlang') # 几何滤波处理 geometri_gass_img = cross_plot(geometric_filter, gassi_img, [3, 5, 7], 'geometric-gassi') geometri_pepper_img = cross_plot(geometric_filter, salt_pepper_img, [3, 5, 7], 'geometric-salt_pepper') geometri_mean_img = cross_plot(geometric_filter, mean_img, [3, 5, 7], 'geometric-mean') geometri_rayleigh_img = cross_plot(geometric_filter, rayleigh_img, [3, 5, 7], 'geometric-rayleigh') geometri_erlang_img = cross_plot(geometric_filter, erlang_img, [3, 5, 7], 'geometric-erlang') # 谐波滤波处理 harmonic_gass_img = cross_plot(harmonic_filter, gassi_img, [3, 5, 7], 'harmonic-gassi') harmonic_pepper_img = cross_plot(harmonic_filter, salt_pepper_img, [3, 5, 7], 'harmonic-salt_pepper') harmonic_mean_img = cross_plot(harmonic_filter, mean_img, [3, 5, 7], 'harmonic-mean') harmonic_rayleigh_img = cross_plot(harmonic_filter, rayleigh_img, [3, 5, 7], 'harmonic-rayleigh') harmonic_erlang_img = cross_plot(harmonic_filter, erlang_img, [3, 5, 7], 'harmonic-erlang') # 逆谐波滤波处理 contra_gass_img = cross_contra_plot(contra_harmonic_filter, gassi_img, [3, 5, 7], 'contra_harmonic-gassi') contra_pepper_img = cross_contra_plot(contra_harmonic_filter, salt_pepper_img, [3, 5, 7], 'contra_harmonic-salt_pepper') contra_mean_img = cross_contra_plot(contra_harmonic_filter, mean_img, [3, 5, 7], 'contra_harmonic-mean') contra_rayleigh_img = cross_contra_plot(contra_harmonic_filter, rayleigh_img, [3, 5, 7], 'contra_harmonic-rayleigh') contra_erlang_img = cross_contra_plot(contra_harmonic_filter, erlang_img, [3, 5, 7], 'contra_harmonic-erlang') # 最大值滤波处理 max_gass_img = cross_plot(max_filt, gassi_img, [3, 5, 7], 'max-gassi') max_salt_pepper_img = cross_plot(max_filt, salt_pepper_img, [3, 5, 7], 'max-salt_pepper') max_mean_img = cross_plot(max_filt, mean_img, [3, 5, 7], 'max-mean') max_rayleigh_img = cross_plot(max_filt, rayleigh_img, [3, 5, 7], 'max-rayleigh') max_erlang_img = cross_plot(max_filt, erlang_img, [3, 5, 7], 'max-erlang') # 最小值滤波处理 min_gass_img = cross_plot(min_filt, gassi_img, [3, 5, 7], 'min-gassi') min_pepper_img = cross_plot(min_filt, salt_pepper_img, [3, 5, 7], 'min-salt_pepper') min_mean_img = cross_plot(min_filt, mean_img, [3, 5, 7], 'min-mean') min_rayleigh_img = cross_plot(min_filt, rayleigh_img, [3, 5, 7], 'min-rayleigh') min_erlang_img = cross_plot(min_filt, erlang_img, [3, 5, 7], 'min-erlang') # 中值滤波处理 media_gass_img = cross_plot(media_filt, gassi_img, [3, 5, 7], 'media-gassi') media_pepper_img = cross_plot(media_filt, salt_pepper_img, [3, 5, 7], 'media-salt_pepper') media_mean_img = cross_plot(media_filt, mean_img, [3, 5, 7], 'media-mean') media_rayleigh_img = cross_plot(media_filt, rayleigh_img, [3, 5, 7], 'media-rayleigh') media_erlang_img = cross_plot(media_filt, erlang_img, [3, 5, 7], 'media-erlang') # 中点滤波处理 medpoint_gass_img = cross_plot(medpoint_filt, gassi_img, [3, 5, 7], 'medpoint-gassi') medpoint_pepper_img = cross_plot(medpoint_filt, salt_pepper_img, [3, 5, 7], 'medpoint-salt_pepper') medpoint_mean_img = cross_plot(medpoint_filt, mean_img, [3, 5, 7], 'medpoint-mean') medpoint_rayleigh_img = cross_plot(medpoint_filt, rayleigh_img, [3, 5, 7], 'medpoint-rayleigh') medpoint_erlang_img = cross_plot(medpoint_filt, erlang_img, [3, 5, 7], 'medpoint-erlang') # 自适应均值滤波处理 autofit_gassi_img = cross_autofit_meanplot(autofit_mean_filt, gassi_img, [3, 5, 7], 'autofit-gassi') autofit_salt_pepper_img = cross_autofit_meanplot(autofit_mean_filt, salt_pepper_img, [3, 5, 7], 'autofit-salt_pepper') autofit_mean_img = cross_autofit_meanplot(autofit_mean_filt, mean_img, [3, 5, 7], 'autofit-mean') autofit_rayleigh_img = cross_autofit_meanplot(autofit_mean_filt, rayleigh_img, [3, 5, 7], 'autofit-rayleigh') autofit_erlang_img = cross_autofit_meanplot(autofit_mean_filt, erlang_img, [3, 5, 7], 'autofit-erlang') # alpha滤波处理 alpha_gassi_img = cross_alpha_plot(alpha_filter, gassi_img, [2, 4, 6], 'alpha-gassi') alpha_salt_pepper_img = cross_alpha_plot(alpha_filter, salt_pepper_img, [2, 4, 6], 'alpha-salt_pepper') alpha_mean_img = cross_alpha_plot(alpha_filter, mean_img, [2, 4, 6], 'alpha-mean') alpha_rayleigh_img = cross_alpha_plot(alpha_filter, rayleigh_img, [2, 4, 6], 'alpha-rayleigh') alpha_erlang_img = cross_alpha_plot(alpha_filter, erlang_img, [2, 4, 6], 'alpha-erlang') print('====================nsr=================') plot_dB([3, 5, 7], mean_gass_img, 'mean_gass-filtisize', func=count_mse) plot_dB([3, 5, 7], mean_salt_pepper_img, 'mean_salt_pepper-filtsize', func=count_mse) plot_dB([3, 5, 7], mean_mean_img, 'mean_mean-filtsize', func=count_mse) plot_dB([3, 5, 7], mean_rayleigh_img, 'mean_rayleigh-filtsize', func=count_mse) plot_dB([3, 5, 7], mean_erlang_img, 'mean_erlang-filtsize', func=count_mse) plot_dB([3, 5, 7], geometri_gass_img, 'geometri_gass-filtsize', func=count_mse) plot_dB([3, 5, 7], geometri_pepper_img, 'geometri_pepper-filtsize', func=count_mse) plot_dB([3, 5, 7], geometri_mean_img, 'geometri_mean-fitlsize', func=count_mse) plot_dB([3, 5, 7], geometri_rayleigh_img, 'geometri_rayleigh-filtsize', func=count_mse) plot_dB([3, 5, 7], geometri_erlang_img, 'geometri_erlang-filtsize', func=count_mse) plot_dB([3, 5, 7], harmonic_gass_img, 'harmonic_gass-filtsize', func=count_mse) plot_dB([3, 5, 7], harmonic_pepper_img, 'harmonic_pepper-filtsize', func=count_mse) plot_dB([3, 5, 7], harmonic_mean_img, 'harmonic_mean-filtsize', func=count_mse) plot_dB([3, 5, 7], harmonic_rayleigh_img, 'harmonic_rayleigh-filtsize', func=count_mse) plot_dB([3, 5, 7], harmonic_erlang_img, 'harmonic_erlang-filtsize', func=count_mse) plot_dB([3, 5, 7], contra_gass_img, 'contra_gass-filtsize', func=count_mse) plot_dB([3, 5, 7], contra_pepper_img, 'contra_pepper-filtsize', func=count_mse) plot_dB([3, 5, 7], contra_mean_img, 'contra_mean-filtsize', func=count_mse) plot_dB([3, 5, 7], contra_rayleigh_img, 'contra_rayleigh-filtsize', func=count_mse) plot_dB([3, 5, 7], contra_erlang_img, 'contra_erlang-filtsize', func=count_mse) plot_dB([3, 5, 7], max_gass_img, 'max_gass-filtsize', func=count_mse) plot_dB([3, 5, 7], max_salt_pepper_img, 'max_salt_pepper-filtsize', func=count_mse) plot_dB([3, 5, 7], max_mean_img, 'max_mean-filtsize', func=count_mse) plot_dB([3, 5, 7], max_rayleigh_img, 'max_rayleigh-filtsize', func=count_mse) plot_dB([3, 5, 7], max_erlang_img, 'max_erlang-filtsize', func=count_mse) plot_dB([3, 5, 7], min_gass_img, 'min_gass-filtsize', func=count_mse) plot_dB([3, 5, 7], min_pepper_img, 'min_pepper-filtsize', func=count_mse) plot_dB([3, 5, 7], min_mean_img, 'min_mean-filtsize', func=count_mse) plot_dB([3, 5, 7], min_rayleigh_img, 'min_rayleigh-filtsize', func=count_mse) plot_dB([3, 5, 7], min_erlang_img, 'min_erlang-filtsize', func=count_mse) plot_dB([3, 5, 7], media_gass_img, 'media_gass-filtsize', func=count_mse) plot_dB([3, 5, 7], media_pepper_img, 'media_pepper-filtsize', func=count_mse) plot_dB([3, 5, 7], media_mean_img, 'media_mean-filtsize', func=count_mse) plot_dB([3, 5, 7], media_rayleigh_img, 'media_rayleigh-filtsize', func=count_mse) plot_dB([3, 5, 7], media_erlang_img, 'media_erlang-filtsize', func=count_mse) plot_dB([3, 5, 7], medpoint_gass_img, 'medpoint_gass-filtsize', func=count_mse) plot_dB([3, 5, 7], medpoint_pepper_img, 'medpoint_pepper-filtsize', func=count_mse) plot_dB([3, 5, 7], medpoint_mean_img, 'medpoint_mean-filtsize', func=count_mse) plot_dB([3, 5, 7], medpoint_rayleigh_img, 'medpoint_rayleigh-filtsize', func=count_mse) plot_dB([3, 5, 7], medpoint_erlang_img, 'medpoint_erlang-filtsize', func=count_mse) plot_dB([2, 4, 6], alpha_gassi_img, 'alpha_gass-filtsize', func=count_mse) plot_dB([2, 4, 6], alpha_salt_pepper_img, 'alpha_pepper-filtsize', func=count_mse) plot_dB([2, 4, 6], alpha_mean_img, 'alpha_mean-filtsize', func=count_mse) plot_dB([2, 4, 6], alpha_rayleigh_img, 'alpha_rayleigh-filtsize', func=count_mse) plot_dB([2, 4, 6], alpha_erlang_img, 'alpha_erlang-filtsize', func=count_mse) plot_dB([3, 5, 7], autofit_gassi_img, 'autofit_gass-filtsize', func=count_mse) plot_dB([3, 5, 7], autofit_salt_pepper_img, 'autofit_pepper-filtsize', func=count_mse) plot_dB([3, 5, 7], autofit_mean_img, 'autofit_mean-filtsize', func=count_mse) plot_dB([3, 5, 7], autofit_rayleigh_img, 'autofit_rayleigh-filtsize', func=count_mse) plot_dB([3, 5, 7], autofit_erlang_img, 'autofit_erlang-filtsize', func=count_mse) print('===================nsr==================') plot_dB([3, 5, 7], mean_gass_img, 'mean_gass-filtisize') plot_dB([3, 5, 7], mean_salt_pepper_img, 'mean_salt_pepper-filtsize') plot_dB([3, 5, 7], mean_mean_img, 'mean_mean-filtsize') plot_dB([3, 5, 7], mean_rayleigh_img, 'mean_rayleigh-filtsize') plot_dB([3, 5, 7], mean_erlang_img, 'mean_erlang-filtsize') plot_dB([3, 5, 7], geometri_gass_img, 'geometri_gass-filtsize') plot_dB([3, 5, 7], geometri_pepper_img, 'geometri_pepper-filtsize') plot_dB([3, 5, 7], geometri_mean_img, 'geometri_mean-fitlsize') plot_dB([3, 5, 7], geometri_rayleigh_img, 'geometri_rayleigh-filtsize') plot_dB([3, 5, 7], geometri_erlang_img, 'geometri_erlang-filtsize') plot_dB([3, 5, 7], harmonic_gass_img, 'harmonic_gass-filtsize') plot_dB([3, 5, 7], harmonic_pepper_img, 'harmonic_pepper-filtsize') plot_dB([3, 5, 7], harmonic_mean_img, 'harmonic_mean-filtsize') plot_dB([3, 5, 7], harmonic_rayleigh_img, 'harmonic_rayleigh-filtsize') plot_dB([3, 5, 7], harmonic_erlang_img, 'harmonic_erlang-filtsize') plot_dB([3, 5, 7], contra_gass_img, 'contra_gass-filtsize') plot_dB([3, 5, 7], contra_pepper_img, 'contra_pepper-filtsize') plot_dB([3, 5, 7], contra_mean_img, 'contra_mean-filtsize') plot_dB([3, 5, 7], contra_rayleigh_img, 'contra_rayleigh-filtsize') plot_dB([3, 5, 7], contra_erlang_img, 'contra_erlang-filtsize') plot_dB([3, 5, 7], max_gass_img, 'max_gass-filtsize') plot_dB([3, 5, 7], max_salt_pepper_img, 'max_salt_pepper-filtsize') plot_dB([3, 5, 7], max_mean_img, 'max_mean-filtsize') plot_dB([3, 5, 7], max_rayleigh_img, 'max_rayleigh-filtsize') plot_dB([3, 5, 7], max_erlang_img, 'max_erlang-filtsize') plot_dB([3, 5, 7], min_gass_img, 'min_gass-filtsize') plot_dB([3, 5, 7], min_pepper_img, 'min_pepper-filtsize') plot_dB([3, 5, 7], min_mean_img, 'min_mean-filtsize') plot_dB([3, 5, 7], min_rayleigh_img, 'min_rayleigh-filtsize') plot_dB([3, 5, 7], min_erlang_img, 'min_erlang-filtsize') plot_dB([3, 5, 7], media_gass_img, 'media_gass-filtsize') plot_dB([3, 5, 7], media_pepper_img, 'media_pepper-filtsize') plot_dB([3, 5, 7], media_mean_img, 'media_mean-filtsize') plot_dB([3, 5, 7], media_rayleigh_img, 'media_rayleigh-filtsize') plot_dB([3, 5, 7], media_erlang_img, 'media_erlang-filtsize') plot_dB([3, 5, 7], medpoint_gass_img, 'medpoint_gass-filtsize') plot_dB([3, 5, 7], medpoint_pepper_img, 'medpoint_pepper-filtsize') plot_dB([3, 5, 7], medpoint_mean_img, 'medpoint_mean-filtsize') plot_dB([3, 5, 7], medpoint_rayleigh_img, 'medpoint_rayleigh-filtsize') plot_dB([3, 5, 7], medpoint_erlang_img, 'medpoint_erlang-filtsize') plot_dB([2, 4, 6], alpha_gassi_img, 'alpha_gass-filtsize') plot_dB([2, 4, 6], alpha_salt_pepper_img, 'alpha_pepper-filtsize') plot_dB([2, 4, 6], alpha_mean_img, 'alpha_mean-filtsize') plot_dB([2, 4, 6], alpha_rayleigh_img, 'alpha_rayleigh-filtsize') plot_dB([2, 4, 6], alpha_erlang_img, 'alpha_erlang-filtsize') plot_dB([3, 5, 7], autofit_gassi_img, 'autofit_gass-filtsize') plot_dB([3, 5, 7], autofit_salt_pepper_img, 'autofit_pepper-filtsize') plot_dB([3, 5, 7], autofit_mean_img, 'autofit_mean-filtsize') plot_dB([3, 5, 7], autofit_rayleigh_img, 'autofit_rayleigh-filtsize') plot_dB([3, 5, 7], autofit_erlang_img, 'autofit_erlang-filtsize') print('=================mse===================') plot_dB([3, 5, 7], mean_gass_img, 'mean_gass-filtisize', func=count_feek_dB) plot_dB([3, 5, 7], mean_salt_pepper_img, 'mean_salt_pepper-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], mean_mean_img, 'mean_mean-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], mean_rayleigh_img, 'mean_rayleigh-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], mean_erlang_img, 'mean_erlang-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], geometri_gass_img, 'geometri_gass-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], geometri_pepper_img, 'geometri_pepper-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], geometri_mean_img, 'geometri_mean-fitlsize', func=count_feek_dB) plot_dB([3, 5, 7], geometri_rayleigh_img, 'geometri_rayleigh-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], geometri_erlang_img, 'geometri_erlang-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], harmonic_gass_img, 'harmonic_gass-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], harmonic_pepper_img, 'harmonic_pepper-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], harmonic_mean_img, 'harmonic_mean-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], harmonic_rayleigh_img, 'harmonic_rayleigh-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], harmonic_erlang_img, 'harmonic_erlang-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], contra_gass_img, 'contra_gass-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], contra_pepper_img, 'contra_pepper-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], contra_mean_img, 'contra_mean-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], contra_rayleigh_img, 'contra_rayleigh-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], contra_erlang_img, 'contra_erlang-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], max_gass_img, 'max_gass-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], max_salt_pepper_img, 'max_salt_pepper-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], max_mean_img, 'max_mean-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], max_rayleigh_img, 'max_rayleigh-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], max_erlang_img, 'max_erlang-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], min_gass_img, 'min_gass-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], min_pepper_img, 'min_pepper-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], min_mean_img, 'min_mean-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], min_rayleigh_img, 'min_rayleigh-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], min_erlang_img, 'min_erlang-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], media_gass_img, 'media_gass-filtsize, func=count_feek_dB') plot_dB([3, 5, 7], media_pepper_img, 'media_pepper-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], media_mean_img, 'media_mean-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], media_rayleigh_img, 'media_rayleigh-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], media_erlang_img, 'media_erlang-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], medpoint_gass_img, 'medpoint_gass-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], medpoint_pepper_img, 'medpoint_pepper-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], medpoint_mean_img, 'medpoint_mean-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], medpoint_rayleigh_img, 'medpoint_rayleigh-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], medpoint_erlang_img, 'medpoint_erlang-filtsize', func=count_feek_dB) plot_dB([2, 4, 6], alpha_gassi_img, 'alpha_gass-filtsize', func=count_feek_dB) plot_dB([2, 4, 6], alpha_salt_pepper_img, 'alpha_pepper-filtsize', func=count_feek_dB) plot_dB([2, 4, 6], alpha_mean_img, 'alpha_mean-filtsize', func=count_feek_dB) plot_dB([2, 4, 6], alpha_rayleigh_img, 'alpha_rayleigh-filtsize', func=count_feek_dB) plot_dB([2, 4, 6], alpha_erlang_img, 'alpha_erlang-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], autofit_gassi_img, 'autofit_gass-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], autofit_salt_pepper_img, 'autofit_pepper-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], autofit_mean_img, 'autofit_mean-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], autofit_rayleigh_img, 'autofit_rayleigh-filtsize', func=count_feek_dB) plot_dB([3, 5, 7], autofit_erlang_img, 'autofit_erlang-filtsize', func=count_feek_dB)