超分辨率算法python源码+项目说明.zip

# coding:utf-8 __author__ = 'liangz14' import math import numpy as np from skimage import io from skimage import color import scipy.io from scipy.misc import imresize from scipy.signal import convolve2d from sklearn.externals import joblib from skimage import data #求解线性方程组： from sklearn import linear_model import bicubic_2d import matplotlib.pyplot as plt import skimage from skimage.filters import gaussian_filter from skimage.morphology import disk from skimage.filters.rank import median #超分辨率处理函数 def SR(img,Dh, Dl,l_da=0.0001, scale=3.0,patch_sizel=3.0, overlap=1.0 ,img_size=1.0): #l_da是经验值 #得到特征域 feat_scale=2.0 patch_sizeh = patch_sizel*scale patch_sizem = patch_sizel*feat_scale imgm = bicubic_2d.bicubic2d(img, feat_scale) imgh = np.zeros((img.shape[0]*3,img.shape[1]*3),dtype='float64') img_bicubic = bicubic_2d.bicubic2d(img, 3.0) normalizationMat = np.zeros((img.shape[0]*3,img.shape[1]*3)) print img.shape, imgm.shape, imgh.shape f1 = np.asarray([[-1, 0, 1]], dtype='float64') f2 = np.asarray([[-1], [0], [1]], dtype='float64') f3 = np.asarray([[1, 0, -2, 0, 1]], dtype='float64') f4 = np.asarray([[1], [0], [-2], [0], [1]], dtype='float64') F = np.zeros((imgm.shape[0], imgm.shape[1], 4)) F[:, :, 0] = convolve2d(imgm, f1, mode='same') F[:, :, 1] = convolve2d(imgm, f2, mode='same') F[:, :, 2] = convolve2d(imgm, f3, mode='same') F[:, :, 3] = convolve2d(imgm, f4, mode='same') xgrid = np.ogrid[np.ceil(patch_sizel/2.0):img.shape[1]-patch_sizel:patch_sizel - overlap] ygrid = np.ogrid[np.ceil(patch_sizel/2.0):img.shape[0]-patch_sizel:patch_sizel - overlap] xgrid = np.asarray(xgrid) ygrid = np.asarray(ygrid) xgrid = np.append(xgrid, img.shape[1]-patch_sizel) ygrid = np.append(ygrid, img.shape[0]-patch_sizel) xgridm = (xgrid-1)*feat_scale + 1 ygridm = (ygrid-1)*feat_scale + 1 xgridh = (xgrid-1)*scale+1 ygridh = (ygrid-1)*scale+1 for i in range(len(xgridm)): for j in range(len(ygridm)): x,y = xgridm[i], ygridm[j] xh,yh = xgridh[i], ygridh[j] patchm = imgm[y:y+patch_sizem, x:x+patch_sizem] avg_patch = np.mean(patchm) featpatch = np.transpose(F[y:y+patch_sizem, x:x+patch_sizem,:],axes=(2,0,1)).reshape(patch_sizem*patch_sizem*4) normalization_m = math.sqrt(np.sum(featpatch**2)) yy=[] if normalization_m > 1: yy = featpatch/normalization_m else: yy = featpatch clf = linear_model.Lasso(alpha=l_da) clf.fit(Dl, yy) w = clf.coef_ patchh=[] if normalization_m > 1: patchh = np.dot(Dh,w)*normalization_m else: patchh = np.dot(Dh,w) patchh = patchh.reshape([patch_sizeh, patch_sizeh]) + avg_patch imgh[yh:yh+patch_sizeh, xh:xh+patch_sizeh]= imgh[yh:yh+patch_sizeh, xh:xh+patch_sizeh] + patchh normalizationMat[yh:yh+patch_sizeh, xh:xh+patch_sizeh]= normalizationMat[yh:yh+patch_sizeh, xh:xh+patch_sizeh]+ 1 print x,y mask = normalizationMat == 0 normalizationMat[mask] = 1 imgh = imgh/normalizationMat imgh[mask] = img_bicubic[mask] joblib.dump(imgh, 'img_h0005.pkl') return imgh def readSR(): return joblib.load('img_h0005.pkl') def psnr( img1,img2): mse = np.mean( (img1 - img2) ** 2 ) if mse == 0: return 100 return 20 * math.log10(np.max(img1) / math.sqrt(mse)) #恢复child图像 def m1(): #加载字典 mat = scipy.io.loadmat('./Dictionary/dictionary.mat') Dl_dict = np.asarray( mat['Dl'],dtype='float64') Dh_dict = np.asarray( mat['Dh'],dtype='float64') #提取出L通道进行计算其他通道使用Bicubic进行计算 #src_rgb_img = io.imread('./Data/Child_input.png') src_rgb_img = data.lena() src = src_rgb_img[:src_rgb_img.shape[0]-src_rgb_img.shape[0]%3,:src_rgb_img.shape[1]-src_rgb_img.shape[0]%3,:] src_rgb_img = src #需要变称散的倍数才能处理 #src_rgb_img = bicubic_2d.bicubic2d(src,1/3.0) src_rgb_img = bicubic_2d.bicubic2d(src_rgb_img,1/3.0) src_rgb_img = src_rgb_img[:src_rgb_img.shape[0]-src_rgb_img.shape[0]%3,:src_rgb_img.shape[1]-src_rgb_img.shape[0]%3,:] #print src_rgb_img.shape src_Lab_img = color.rgb2lab(src_rgb_img) src_Lab_img_L = src_Lab_img[:,:,0] src_Lab_img_a = src_Lab_img[:,:,1] src_Lab_img_b = src_Lab_img[:,:,2] #不同的L通道来源 #dst_Lab_img_L = readSR() #m = dst_Lab_img_L<0 #dst_Lab_img_L[m] = 0 #print np.max(dst_Lab_img_L),np.min(dst_Lab_img_L) #dst_Lab_img_L = SR(src_Lab_img_L,Dh_dict,Dl_dict) dst_Lab_img_L = imresize(src_Lab_img_L,3.0,'bicubic') #dst_Lab_img_L= dst_Lab_img_L/255.0*(np.max(src_Lab_img_L)-np.min(src_Lab_img_L))+np.min(src_Lab_img_L) dst_Lab_img_a = imresize(src_Lab_img_a,3.0,'bicubic') dst_Lab_img_a= dst_Lab_img_a/255.0*(np.max(src_Lab_img_a)-np.min(src_Lab_img_a))+np.min(src_Lab_img_a) dst_Lab_img_b = imresize(src_Lab_img_b,3.0,'bicubic') dst_Lab_img_b= dst_Lab_img_b/255.0*(np.max(src_Lab_img_b)-np.min(src_Lab_img_b))+np.min(src_Lab_img_b) img_lab = np.zeros((dst_Lab_img_L.shape[0],dst_Lab_img_L.shape[1],3)) img_lab[:,:,0] = dst_Lab_img_L img_lab[:,:,1] = dst_Lab_img_a img_lab[:,:,2] = dst_Lab_img_b dst = color.lab2rgb(img_lab) #src = src[:dst.shape[0],:dst.shape[1]] #print np.mean(dst[10:-15,10:-15]*255-src[10:-15,10:-15]) #print psnr(dst[10:-15,10:-15]*255,src[10:-15,10:-15]) plt.imshow(dst,interpolation="none") plt.show() #回复lena图像并进行比较 def m2(): #加载字典 mat = scipy.io.loadmat('./Dictionary/dictionary.mat') Dl_dict = np.asarray( mat['Dl'],dtype='float64') Dh_dict = np.asarray( mat['Dh'],dtype='float64') #提取出L通道进行计算其他通道使用Bicubic进行计算 src_rgb_img = io.imread('./Data/Child_input.png') src_yCrCb = colormanage.rgb2ycbcr(src_rgb_img) src_Ycbcr_img_Y = src_yCrCb[:,:,0] src_Ycbcr_img_Cb = src_yCrCb[:,:,1] src_Ycbcr_img_Cr = src_yCrCb[:,:,2] src_Ycbcr_img_Y = src_Ycbcr_img_Y[:src_yCrCb.shape[0]-src_yCrCb.shape[0]%3,:src_yCrCb.shape[1]-src_yCrCb.shape[1]%3] src_Ycbcr_img_Cb = src_Ycbcr_img_Cb[:src_yCrCb.shape[0]-src_yCrCb.shape[0]%3,:src_yCrCb.shape[1]-src_yCrCb.shape[1]%3] src_Ycbcr_img_Cr = src_Ycbcr_img_Cr[:src_yCrCb.shape[0]-src_yCrCb.shape[0]%3,:src_yCrCb.shape[1]-src_yCrCb.shape[1]%3] #不同的L通道来源 #dst_Lab_img_L = readSR() #dst_YCbCr_img_Y = SR(src_Ycbcr_img_Y,Dh_dict,Dl_dict) dst_YCbCr_img_Y = bicubic_2d.bicubic2d(src_Ycbcr_img_Y,3.0) dst_YCbCr_img_Cb = bicubic_2d.bicubic2d(src_Ycbcr_img_Cb,3.0) dst_YCbCr_img_Cr = bicubic_2d.bicubic2d(src_Ycbcr_img_Cr,3.0) img_YCbCr = np.zeros((dst_YCbCr_img_Y.shape[0],dst_YCbCr_img_Y.shape[1],3)) img_YCbCr[:,:,0] = dst_YCbCr_img_Y img_YCbCr[:,:,1] = dst_YCbCr_img_Cb img_YCbCr[:,:,2] = dst_YCbCr_img_Cr dst = colormanage.ycbcr2rgb(img_YCbCr) joblib.dump(dst,'child_result_bicubic_3x_ycbcr.pkl') plt.imshow(np.asarray(dst,dtype='uint8'),interpolation="none") plt.show() #回复lena图像,尝试在不同的状态下进行比较得到的结果，原始图像是ycbcr空间 import colormanage def m3(): #加载字典 mat = scipy.io.loadmat('./Dictionary/dictionary.mat') Dl_dict = np.asarray( mat['Dl'],dtype='float64') Dh_dict = np.asarray( mat['Dh'],dtype='float64') #提取出L通道进行计算其他通道使用Bicubic进行计算 src = data.lena() src_yCrCb = colormanage.rgb2ycbcr(src) src_Ycbcr_img_Y = bicubic_2d.bicubic2d(src_yCrCb[:,:,0],1/3.0) src_Ycbcr_img_Cb = bicubic_2d.bicubic2d(src_yCrCb[:,:,1],1/3.0) src_Ycbcr_img_Cr = bicubic_2d.bicubic2d(src_yCrCb[:,:,2],1/3.0)