问题描述
在图像处理中,对图像进行傅立叶变换,然后进行傅立叶中心变换,然后进行欠采样,然后返回到逆中心变换,再通过逆傅立叶变换返回图像。那么,要计算点扩散函数,下面的函数还可以吗?
def apply_Fu(sampling_pattern,x):
#compute subsampled FFT #Sampling pattern is matrix where 1 means we take that sample for undersampling
return sampling_pattern*np.fft.fftshift(np.fft.fft2(x))
def apply_Fu_adjoint(sampling_pattern,y):
#Compute adjoing of subsampled k space
return np.fft.ifft2(np.fft.ifftshift(sampling_pattern*y))
def spr(gridsize,sampling_pattern):
# gridsize should be 2-element tuple,e.g. gridsize = (10,10)
maxima = np.zeros(gridsizedtype = np.complex_)
for x in range(gridsize[0]):
for y in range(gridsize[1]):
# in this iteration,the index "i" corresponds to the gridpoint (x,y)
# construct basis vector
e_i = np.zeros(gridsize,dtype = np.complex_)
e_i[x,y] = 1
# compute psf_i = Fu* Fu e_i
# psf_i[xx,yy] is PSF(i,j) if index "j" corresponds to gridpoint (xx,yy)
psf_i = apply_Fu_adjoint(sampling_pattern,apply_Fu(sampling_pattern,e_i))
# normalize; psf_i[x,y] is PSF(i,i)
psf_i = psf_i / psf_i[x,y]
# trick to exclude point "i" itself from maximum: set it to -infinity
psf_i[x,y] = -np.inf
# "inner" maximum,over "j"
maxima[x,y] = np.max(psf_i)
spr = np.max(maxima)
return spr
spr = spr(img.shape,random)
np.abs(spr)```
解决方法
您可以使用此 https://github.com/cgohlke/psf,一个用于荧光显微镜的点扩散函数计算