超分辨率图像重建技术分为基于插值、基于重建、基于学习三类。基于插值的方法包括最近邻插值法、双线性插值法和双立方插值法等。这里主要介绍基于双三次插值的实现，其他的网络资料丰富，不再赘述。
代码实现 pip3 install opencv-contrib-pythonpip3 install numpy import cv2import numpy as npimport mathimport sys, timeimport os# Interpolation kernel 插值内核def u(s,a): if (abs(s) >=0) & (abs(s) <=1): return (a+2)*(abs(s)**3)-(a+3)*(abs(s)**2)+1 elif (abs(s) > 1) & (abs(s) <= 2): return a*(abs(s)**3)-(5*a)*(abs(s)**2)+(8*a)*abs(s)-4*a return 0#Paddnigdef padding(img,H,W,C): zimg = np.zeros((H+4,W+4,C)) zimg[2:H+2,2:W+2,:C] = img #Pad the first/last two col and row zimg[2:H+2,0:2,:C]=img[:,0:1,:C] zimg[H+2:H+4,2:W+2,:]=img[H-1:H,:,:] zimg[2:H+2,W+2:W+4,:]=img[:,W-1:W,:] zimg[0:2,2:W+2,:C]=img[0:1,:,:C] #Pad the missing eight points zimg[0:2,0:2,:C]=img[0,0,:C] zimg[H+2:H+4,0:2,:C]=img[H-1,0,:C] zimg[H+2:H+4,W+2:W+4,:C]=img[H-1,W-1,:C] zimg[0:2,W+2:W+4,:C]=img[0,W-1,:C] return zimgdef get_progressbar_str(progress): END = 170 MAX_LEN = 30 BAR_LEN = int(MAX_LEN * progress) return ('Progress:[' + '=' * BAR_LEN + ('>' if BAR_LEN < MAX_LEN else '') + ' ' * (MAX_LEN - BAR_LEN) + '] %.1f%%' % (progress * 100.))# Bicubic operation 双三次插值def bicubic(img, ratio, a): #Get image size H,W,C = img.shape img = padding(img,H,W,C) #Create new image dH = math.floor(H*ratio) dW = math.floor(W*ratio) dst = np.zeros((dH, dW, 3)) h = 1/ratio print('Start bicubic interpolation') print('It will take a little while...') inc = 0 for c in range(C): for j in range(dH): for i in range(dW): x, y = i * h + 2 , j * h + 2 x1 = 1 + x - math.floor(x) x2 = x - math.floor(x) x3 = math.floor(x) + 1 - x x4 = math.floor(x) + 2 - x y1 = 1 + y - math.floor(y) y2 = y - math.floor(y) y3 = math.floor(y) + 1 - y y4 = math.floor(y) + 2 - y mat_l = np.matrix([[u(x1,a),u(x2,a),u(x3,a),u(x4,a)]]) mat_m = np.matrix([[img[int(y-y1),int(x-x1),c],img[int(kwdxwz),int(x-x1),c],img[int(y+y3),int(x-x1),c],img[int(y+y4),int(x-x1),c]], [img[int(y-y1),int(x-x2),c],img[int(kwdxwz),int(x-x2),c],img[int(y+y3),int(x-x2),c],img[int(y+y4),int(x-x2),c]], [img[int(y-y1),int(x+x3),c],img[int(kwdxwz),int(x+x3),c],img[int(y+y3),int(x+x3),c],img[int(y+y4),int(x+x3),c]], [img[int(y-y1),int(x+x4),c],img[int(kwdxwz),int(x+x4),c],img[int(y+y3),int(x+x4),c],img[int(y+y4),int(x+x4),c]]]) mat_r = np.matrix([[u(y1,a)],[u(y2,a)],[u(y3,a)],[u(y4,a)]]) dst[j, i, c] = np.dot(np.dot(mat_l, mat_m),mat_r) # Print progress inc = inc + 1 sys.stderr.write('r 33[K' + get_progressbar_str(inc/(C*dH*dW))) sys.stderr.flush() sys.stderr.write('n') sys.stderr.flush() return dst# Scale factor ratio = 4 # 放大倍数为4# Coefficienta = -1/2# Read imageimg = cv2.imread('comic_LR.png')dst = bicubic(img, ratio, a)print('Completed!')cv2.imwrite( 'bicubic_' + 'comic_LR.png' , dst) 实验效果 原图四倍重建放大