图像噪声滤除，增加

文章目录

• 图像噪声滤除，增加
• • 灰度图像
  • 彩色图像
  • 图像仿射变换

图像噪声滤除，增加

灰度图像

import cv2
import numpy as np
from matplotlib import pyplot as plt
from skimage import util,color
import math
import random
import datetime
import pywt## for color image show with plt
def img_plt(img):b,g,r = cv2.split(img)img = cv2.merge([r, g, b])return img
######  add salt & peckle noise
def sp_noise(img,prob=0.02):dst_img = np.array(img,np.uint8)row = img.shape[0]col = img.shape[1]mn = row*colrp_num = math.floor(row*col*prob)rp = random.sample(range(0, mn), rp_num)for i in range(rp_num):pixel_v = 255 * np.random.randint(0, 2, size=1)m = math.floor(rp[i]/col)n = rp[i]%colif len(img.shape)>2:dst_img[m,n,:] = pixel_velse:dst_img[m,n] = pixel_vreturn dst_img
###### add gaussian noise
def gauss_noise(img, mean=0, var=0.001):image = np.array(img/255, dtype=float)noise = np.random.normal(mean, var ** 0.5, img.shape)dst_img = image + noiseif dst_img.min() < 0:low_clip = -1.else:low_clip = 0.dst_img = np.clip(dst_img, low_clip, 1.0)dst_img = np.uint8(dst_img*255)return dst_img#####  Inverse modulation and mean filter
###    the parameters' meaning are:first-img data, second-K
###    third-Statistics area size
def Inverse_modulation(img,k,h1,v1):r = img.shape[0]c = img.shape[1]h = math.floor(h1 / 2)  ## threshold of rowv = math.floor(v1 / 2)  ## threshold of Columndst_img = np.array(img)for i in range(h,r-h):for j in range(v,c-v):if len(img.shape)==2:sum_k1 = np.sum(np.float32(img[i-h:i+h+1,j-v:j+v+1]) **(k+1))sum_k = np.sum(np.float32(img[i - h:i + h + 1, j - v:j + v + 1]) ** (k))dst_img[i][j] =abs(sum_k1/sum_k)else:for t in range(img.shape[3]):sum_k1 = np.sum(np.float32(img[i-h:i+h+1,j-v:j+v+1,t]) **(k+1))sum_k = np.sum(np.float32(img[i - h:i + h + 1, j - v:j + v + 1,t]) ** (k))dst_img[i,j,t] =abs(sum_k1/sum_k)return dst_img# ########## noise addition and filtering
# if __name__ == '__main__':
#     img = cv2.imread('lena-color.jpg',-1)
#     img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#     noise_sp_img = sp_noise(img,0.01)
#     noise_gs_img = gauss_noise(img)
#     # img = img_plt(img)
#     plt.subplot(331), plt.imshow(img,cmap='gray'), plt.title('Original'),plt.xticks([]), plt.yticks([])
#     # add s&p noise
#     # noise_sp_img = img_plt(noise_sp_img)
#     plt.subplot(332), plt.imshow(noise_sp_img,cmap='gray'), plt.title('s&p'),plt.xticks([]), plt.yticks([])
#     # add gaussian noise
#     # noise_gs_img = img_plt(noise_gs_img)
#     plt.subplot(333),plt.imshow(noise_gs_img,cmap='gray'),plt.title('gaussian'),plt.xticks([]), plt.yticks([])
#
#     # noise_gs_img=util.random_noise(img,mode='s&p')
#     # noise_sp_img=noise_gs_img*255  #由于输出是[0，1]的浮点型，先转成灰度图（我的输入就是灰度图）
#     # noise_sp_img=noise_gs_img.astype(np.int)   #再变成整型数组
#     # cv2.imwrite(os.path.join(save_dir,image_name),noise_sp_img)  #保存到新建的文件夹
#
#     # the fft result of original image
#     f2 = np.fft.fft2(img)
#     fshift = np.fft.fftshift(f2)
#     original_spectrum = 20*np.log(np.abs(fshift))
#     plt.subplot(334),plt.imshow(original_spectrum,cmap='gray'),plt.title('original_spectrum')
#     plt.xticks([]), plt.yticks([])
#     # the fft result of the image with s&p noise
#     f2 = np.fft.fft2(noise_sp_img)
#     fshift = np.fft.fftshift(f2)
#     sp_spectrum = 20*np.log(np.abs(fshift))
#     plt.subplot(335),plt.imshow(sp_spectrum,cmap='gray'),plt.title('s&p_spectrum')
#     plt.xticks([]), plt.yticks([])
#
#     # the fft result of the image with gaussian noise
#     f2 = np.fft.fft2(noise_gs_img)
#     fshift = np.fft.fftshift(f2)
#     gaussian_spectrum = 20*np.log(np.abs(fshift))
#     plt.subplot(336),plt.imshow(gaussian_spectrum,cmap='gray'),plt.title('gaussian_spectrum')
#     plt.xticks([]), plt.yticks([])
#     #
#     # #####  use the Inverse modulation
#     img_inver = Inverse_modulation(img,1,5,5)
#     noise_sp_img = Inverse_modulation(noise_sp_img,1,5,5)
#     noise_gs_img = Inverse_modulation(noise_gs_img,1, 5, 5)
#     plt.subplot(337),plt.imshow(img_inver,cmap='gray'),plt.title('orig_modul')
#     plt.xticks([]), plt.yticks([])
#     plt.subplot(338),plt.imshow(noise_sp_img,cmap='gray'),plt.title('sp_modul')
#     plt.xticks([]), plt.yticks([])
#     plt.subplot(339), plt.imshow(noise_gs_img, cmap='gray'), plt.title('gs_modul')
#     plt.xticks([]), plt.yticks([])
#     plt.show()# 仿真运动模糊
# 第一个参数为图像大小，
# 第二个参数为模糊的角度，
# 第三参数为运动像素点个数
def motion_process(image_size, motion_angle, motion_dis):PSF = np.zeros(image_size)x_center = (image_size[0] - 1) / 2# y_center = (image_size[0] - 1) / 2slope_tan = math.tan(motion_angle * math.pi / 180)slope_cot = 1 / slope_tanif slope_tan <= 1:for i in range(motion_dis):offset = round(i * slope_tan)if int(x_center + offset)<image_size[0] and int(x_center - offset)>0:PSF[int(x_center + offset), int(x_center - offset)] = 1return PSF / PSF.sum()else:for i in range(motion_dis):offset = round(i * slope_cot)if int(x_center + offset) < image_size[0] and int(x_center - offset) > 0:PSF[int(x_center - offset), int(x_center + offset)] = 1return PSF / PSF.sum()# 对图片进行运动模糊
def make_blurred(input, PSF, eps):input_fft = np.fft.fft2(input)  # 进行二维数组的傅里叶变换PSF_fft = np.fft.fft2(PSF) + epsblurred = np.fft.ifft2(input_fft * PSF_fft)blurred = np.abs(np.fft.fftshift(blurred))return blurreddef inverse(input, PSF, eps):  # 逆滤波input_fft = np.fft.fft2(input)PSF_fft = np.fft.fft2(PSF) + eps  # 噪声功率，这是已知的result = np.fft.ifft2(input_fft / PSF_fft)  # 计算F(u,v)的傅里叶反变换result = np.abs(np.fft.fftshift(result))return resultdef wiener(input, PSF, eps, K=0.01):  # 维纳滤波，K=0.01input_fft = np.fft.fft2(input)PSF_fft = np.fft.fft2(PSF) + epsPSF_fft_1 = np.conj(PSF_fft) / (np.abs(PSF_fft) ** 2 + K)result = np.fft.ifft2(input_fft * PSF_fft_1)result = np.abs(np.fft.fftshift(result))return result######### inverse filter and wiener filter
if __name__ == '__main__':image = cv2.imread('lena-color.jpg')image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)img_h = image.shape[0]img_w = image.shape[1]plt.figure(1)plt.xlabel("Original Image")plt.gray()plt.imshow(image)  # 显示原图像plt.figure(2)plt.gray()# 进行运动模糊处理PSF = motion_process((img_h, img_w), 60,15)blurred = np.abs(make_blurred(image, PSF, 1e-3))plt.subplot(231),plt.xlabel("Motion blurred"),plt.imshow(blurred)# 逆滤波result = inverse(blurred, PSF, 1e-3)plt.subplot(232),plt.xlabel("inverse deblurred"),plt.imshow(result)# 维纳滤波result = wiener(blurred, PSF, 1e-3)plt.subplot(233),plt.xlabel("wiener deblurred(k=0.01)"),plt.imshow(result)# 添加噪声,standard_normal产生随机的函数blurred_noisy = blurred + 0.1 * blurred.std() * \np.random.standard_normal(blurred.shape)plt.subplot(234),plt.xlabel("motion & noisy blurred")plt.imshow(blurred_noisy)  # 显示添加噪声且运动模糊的图像# 对添加噪声的图像进行逆滤波result = inverse(blurred_noisy, PSF, 0.1 + 1e-3)plt.subplot(235),plt.xlabel("inverse deblurred"),plt.imshow(result)# 对添加噪声的图像进行维纳滤波result = wiener(blurred_noisy, PSF, 0.1 + 1e-3)plt.subplot(236),plt.xlabel("wiener deblurred(k=0.01)"),plt.imshow(result)plt.show()

彩色图像

import cv2
import numpy as np
from matplotlib import pyplot as plt
import math
from skimage import util,color
import random
import datetime
import pywt## for color image show with plt
def img_plt(img):b,g,r = cv2.split(img)img = cv2.merge([r, g, b])return img
def img_translation(img,tx,ty):dst_img = np.zeros((img.shape),dtype='uint8')for i in range(img.shape[0]):for j in range(img.shape[1]):if i+tx<dst_img.shape[0] and j+ty<dst_img.shape[1]:dst_img[i+tx][j+ty] = img[i][j]return dst_img
def img_resize(img,sx,sy):if len(img.shape)<=2:dst_img = np.zeros((round(img.shape[0]*sx),round(img.shape[1]*sy)),dtype='uint8')else:dst_img = np.zeros((round(img.shape[0] * sx), round(img.shape[1] * sy),img.shape[2]), dtype='uint8')for i in range(dst_img.shape[0]):for j in range(dst_img.shape[1]):if round(i/sx) < img.shape[0] and round(j/sy) < img.shape[1]:dst_img[i][j] = img[round(i/sx)][round(j/sy)]return dst_img
def img_rotation(img,th):dst_img = np.zeros((img.shape), dtype='uint8')row = img.shape[0]col = img.shape[1]# x = x'cos(theta)-y'sin(theta) + m/2*(1-cos(theta))+n/2*sin(theta)# y = x'sin(theta)+y'cos(theta) + n/2*(1-cos(theta))-m/2*sin(theta)for i in range(row):for j in range(col):m = i*math.cos(th)-j*math.sin(th)+row/2*(1-math.cos(th))+col/2*math.sin(th)n = i*math.sin(th)+j*math.cos(th)+col/2*(1-math.cos(th))-row/2*math.sin(th)if m >=0 and m < row and n >=0 and n<col:dst_img[i][j] = img[math.floor(m)][math.floor(n)]return dst_img
##########   color image process  ###########
###   basic process transform   ###
# if __name__ == '__main__':# img = cv2.imread('lena-color.jpg')# # img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# if len(img.shape) > 2:#     m,n,t = img.shape# else:#     m,n = img.shape# Resize_res = cv2.resize(img, None, fx=2, fy=0.5, interpolation=cv2.INTER_CUBIC)# M = cv2.getRotationMatrix2D((m / 2, n / 2), 45, 1)# Rotation_res = cv2.warpAffine(img, M, (n, m))# Translation_res = cv2.warpAffine(img, np.float32([[1, 0, 50], [0, 1, 100]]), (n, m))# my_trans = img_translation(img,100,50)# my_resiz = img_resize(img,0.5,2)# my_rotation = img_rotation(img,-45)# if len(img.shape) > 2:#     img = img_plt(img)#     Resize_res = img_plt(Resize_res)#     Rotation_res = img_plt(Rotation_res)#     Translation_res = img_plt(Translation_res)#     my_trans = img_plt(my_trans)#     my_resiz = img_plt(my_resiz)#     my_rotation = img_plt(my_rotation)#     plt.subplot(241),plt.imshow(img)#     plt.title('original'), plt.xticks([]), plt.yticks([])#     plt.subplot(242),plt.imshow(Resize_res)#     plt.title('resize'), plt.xticks([]), plt.yticks([])#     plt.subplot(243),plt.imshow(Rotation_res)#     plt.title('rotation'), plt.xticks([]), plt.yticks([])#     plt.subplot(244),plt.imshow(Translation_res)#     plt.title('translation'), plt.xticks([]), plt.yticks([])#     plt.subplot(246),plt.imshow(my_resiz)#     plt.title('my resize'), plt.xticks([]), plt.yticks([])#     plt.subplot(247),plt.imshow(my_rotation)#     plt.title('my rotation'), plt.xticks([]), plt.yticks([])#     plt.subplot(248),plt.imshow(my_trans)#     plt.title('my resize'), plt.xticks([]), plt.yticks([])#     plt.show()### if the img is color, then process each channel
def template_conv(img, temp):h1 = temp.shape[0]v1 = temp.shape[1]new_img = np.zeros(img.shape,dtype='uint8')if len(img.shape) != len(temp.shape):if len(img.shape) > 2 and len(temp.shape)<=2:temp_tmp = np.zeros(((h1,v1,img.shape[2])))for k in range(temp_tmp.shape[2]):temp_tmp[:,:,k] = temptemp = temp_tmpelse:print("error: the img or template is not valuable")returnif len(img.shape) != len(temp.shape):print("error: the img or template is not valuable")returnif len(img.shape)>2:for k in range(img.shape[2]):new_img[:,:,k] = template_convolution(img[:,:,k],temp[:,:,k])else:new_img = template_convolution(img, temp)return new_img
def template_convolution(img,temp):r,c = img.shapeh1,v1 = temp.shapeh = math.floor(h1 / 2)  ## threshold of rowv = math.floor(v1 / 2)  ## threshold of ColumnN = sum(sum(temp))dst_img = np.zeros((r,c))for i in range(h,r-h):for j in range(v,c-v):if N !=0:dst_img[i][j] = abs(np.sum(img[i-h:i+h+1,j-v:j+v+1] * temp))/Nelse:dst_img[i][j] = abs(np.sum(img[i-h:i+h+1,j-v:j+v+1] * temp))return dst_imgif __name__ == '__main__':img = cv2.imread('lena-color.jpg')# img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)if len(img.shape) > 2:m,n,t = img.shapeelse:m,n = img.shapet_11 = np.ones((11,11))t_Gaussian = np.array([[1,4,7,4,1],[4,16,26,16,4],[7,26,41,26,7],[4,16,26,16,4],[1,4,5,4,1]])t_Lap = np.array([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]])new_img11 = template_conv(img,t_11)new_imgG = template_conv(img,t_Gaussian)new_imgL = template_conv(img,t_Lap)new_img11 = img_plt(new_img11)new_imgG = img_plt(new_imgG)new_imgL = img_plt(new_imgL)plt.subplot(131),plt.imshow(new_img11)plt.title('Conv 11*11 Average'), plt.xticks([]), plt.yticks([])plt.subplot(132),plt.imshow(new_imgG)plt.title('Conv Gaussian Average'), plt.xticks([]), plt.yticks([])plt.subplot(133),plt.imshow(new_imgL)plt.title('Conv Laplace sharp'), plt.xticks([]), plt.yticks([])plt.show()# if __name__ == '__main__':
# #     img = cv2.imread('lena-color.jpg')
# #     img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# #     if len(img.shape) > 2:
# #         m,n,t = img.shape
# #     else:
# #         m,n = img.shape
# #     ### fft
# #     f = np.fft.fft2(img)
# #     fshift = np.fft.fftshift(f)
# #     np_spectrum = 20 * np.log(np.abs(fshift))
# #     ### inverse fft
# #     ishift = np.fft.ifftshift(fshift)
# #     iimg = np.fft.ifft2(ishift)
# #     iimg = np.abs(iimg)
# #
# #     ### low high pass filter ...
# #     fm = fshift.shape[0]
# #     fn = fshift.shape[1]
# #     dm, dn = fm/4, fn/4# high pass filter# high_mask = np.ones(img.shape,np.uint8)# high_mask[int(m/2-dm/2):int(m/2+dm/2),int(n/2-dn/2):int(n/2+dn/2)] = 0# # low pass filter# low_mask = np.zeros(img.shape,np.uint8)# low_mask[int(m/2-dm/2):int(m/2+dm/2),int(n/2-dn/2):int(n/2+dn/2)] = 1 #低通滤波# # Band pass filter# mask1 = np.ones(img.shape, np.uint8)# mask1[int(m/2-dm/4):int(m/2+dm/4), int(n/2-dn/4):int(n/2+dn/4)] = 0# mask2 = np.zeros(img.shape, np.uint8)# mask2[int(m/2-dm/2):int(m/2+dm/2), int(n/2-dn/2):int(n/2+dn/2)] = 1# mask = mask1 * mask2
# #
# #
# #     low_pass_spectrum = np_spectrum*low_mask
# #     ### the general method
# #     low_pass_filter = fshift*low_mask
# #     ## ifft
# #     ishift = np.fft.ifftshift(low_pass_filter)
# #     iimg_low = np.fft.ifft2(ishift)
# #     iimg_low = np.abs(iimg_low)
# #
# #     ### FFT and IFFT of each channel
# #     # iimg_low = np.zeros(img.shape, np.uint8)
# #     # fshiftr = np.fft.fftshift(np.fft.fft2(img[:,:,0]))
# #     # fshiftg = np.fft.fftshift(np.fft.fft2(img[:, :, 1]))
# #     # fshiftb = np.fft.fftshift(np.fft.fft2(img[:, :, 2]))
# #     # low_pass_filter = fshiftr * low_mask[:, :, 0]
# #     # ishift= np.fft.ifftshift(low_pass_filter)
# #     # r = np.fft.ifft2(ishift)
# #     # r = r.astype(np.uint8)
# #     # low_pass_filter = fshiftg * low_mask[:, :, 1]
# #     # ishift = np.fft.ifftshift(low_pass_filter)
# #     # g = np.fft.ifft2(ishift)
# #     # g = g.astype(np.uint8)
# #     # low_pass_filter = fshiftb * low_mask[:, :, 2]
# #     # ishift = np.fft.ifftshift(low_pass_filter)
# #     # b = np.fft.ifft2(ishift)
# #     # b = b.astype(np.uint8)
# #
# #     # iimg_low = cv2.merge([r, g, b])
# #     # iimg_low = np.abs(iimg_low)
# #
# #     if len(img.shape>2):   ### the bgr to rgb
# #         img = img_plt(img)
# #         np_spectrum = img_plt(np_spectrum)
# #         iimg = img_plt(iimg)
# #         iimg_low = img_plt(iimg_low)
# #
# #
# #     plt.figure().suptitle('Spatial domain image enhancement')
# #     plt.subplot(231),plt.imshow(img)
# #     plt.title('original'), plt.xticks([]), plt.yticks([])
# #     plt.subplot(232),plt.imshow(np_spectrum.astype(np.uint8))
# #     plt.title('np_spectrum'), plt.xticks([]), plt.yticks([])
# #     plt.subplot(233),plt.imshow(iimg.astype(np.uint8))
# #     plt.title('inverse'), plt.xticks([]), plt.yticks([])
# #     plt.subplot(235),plt.imshow(low_pass_spectrum.astype(np.uint8))
# #     plt.title('low pass spectrum'), plt.xticks([]), plt.yticks([])
# #     plt.subplot(236),plt.imshow(iimg_low.astype(np.uint8))
# #     plt.title('low pass inverse '), plt.xticks([]), plt.yticks([])
# #     plt.show()
if __name__ == '__main__':img = cv2.imread('lena-color.jpg')# show BGR lena# plt.subplot(3, 3, 1)# plt.imshow(img)# plt.axis('off')# plt.title('lena_BGR')## # BGR to RGB# lena_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# plt.subplot(3, 3, 2)# plt.imshow(lena_RGB)# plt.axis('off')# plt.title('lena_RGB')## # BGR to GRAY# lena_GRAY = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# plt.subplot(3, 3, 3)# plt.imshow(lena_GRAY,cmap = 'gray')# plt.axis('off')# plt.title('lena_GRAY')## # BGR to CIE XYZ# lena_CIEXYZ = cv2.cvtColor(img, cv2.COLOR_BGR2XYZ)# plt.subplot(3, 3, 4)# plt.imshow(lena_CIEXYZ)# plt.axis('off')# plt.title('lena_CIEXYZ')## # BGR to YCrCb# lena_YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)# plt.subplot(3, 3, 5)# plt.imshow(lena_YCrCb)# plt.axis('off')# plt.title('lena_YCrCb')## # BGR to HSV# lena_HSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)# plt.subplot(3, 3, 6)# plt.imshow(lena_HSV)# plt.axis('off')# plt.title('lena_HSV')## # BGR to HLS# lena_HLS = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)# plt.subplot(3, 3, 7)# plt.imshow(lena_HLS)# plt.axis('off')# plt.title('lena_HLS')## # BGR to CIE L*a*b# lena_Lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)# plt.subplot(3, 3, 8)# plt.imshow(lena_Lab)# plt.axis('off')# plt.title('lena_Lab')## # BGR to CIE L*u*v# lena_Luv = cv2.cvtColor(img, cv2.COLOR_BGR2Luv)# plt.subplot(3, 3, 9)# plt.imshow(lena_Luv)# plt.axis('off')# plt.title('lena_Luv')## plt.show()

图像仿射变换

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