def IHS(pan, hs):
M, N, c = pan.shape
m, n, C = hs.shape
ratio = int(np.round(M / m))
print('get sharpening ratio: ', ratio)
assert int(np.round(M / m)) == int(np.round(N / n))
#upsample
u_hs = upsample_interp23(hs, ratio)
I = np.mean(u_hs, axis=-1, keepdims=True)
P = (pan - np.mean(pan)) * np.std(I, ddof=1) / np.std(pan,
ddof=1) + np.mean(I)
I_IHS = u_hs + np.tile(P - I, (1, 1, C))
#adjustment
I_IHS[I_IHS < 0] = 0
I_IHS[I_IHS > 1] = 1
return I_IHS
def Brovey(pan, hs):
M, N, c = pan.shape
m, n, C = hs.shape
ratio = int(np.round(M / m))
print('get sharpening ratio: ', ratio)
assert int(np.round(M / m)) == int(np.round(N / n))
#upsample
u_hs = upsample_interp23(hs, ratio)
I = np.mean(u_hs, axis=-1)
image_hr = (pan - np.mean(pan)) * (np.std(I, ddof=1) /
np.std(pan, ddof=1)) + np.mean(I)
image_hr = np.squeeze(image_hr)
I_Brovey = []
for i in range(C):
temp = image_hr * u_hs[:, :, i] / (I + 1e-8)
temp = np.expand_dims(temp, axis=-1)
I_Brovey.append(temp)
I_Brovey = np.concatenate(I_Brovey, axis=-1)
#adjustment
I_Brovey[I_Brovey < 0] = 0
I_Brovey[I_Brovey > 1] = 1
return I_Brovey
def GFPCA(pan, hs):
M, N, c = pan.shape
m, n, C = hs.shape
ratio = int(np.round(M / m))
print('get sharpening ratio: ', ratio)
assert int(np.round(M / m)) == int(np.round(N / n))
p = princomp(n_components=C)
pca_hs = p.fit_transform(np.reshape(hs, (m * n, C)))
pca_hs = np.reshape(pca_hs, (m, n, C))
pca_hs = upsample_interp23(pca_hs, ratio)
gp_hs = []
for i in range(C):
temp = guidedFilter(np.float32(pan),
np.float32(np.expand_dims(pca_hs[:, :, i], -1)),
8,
eps=0.001**2)
temp = np.expand_dims(temp, axis=-1)
gp_hs.append(temp)
gp_hs = np.concatenate(gp_hs, axis=-1)
I_GFPCA = p.inverse_transform(gp_hs)
#adjustment
I_GFPCA[I_GFPCA < 0] = 0
I_GFPCA[I_GFPCA > 1] = 1
return I_GFPCA
def SFIM(pan, hs):
M, N, c = pan.shape
m, n, C = hs.shape
ratio = int(np.round(M/m))
print('get sharpening ratio: ', ratio)
assert int(np.round(M/m)) == int(np.round(N/n))
#upsample
u_hs = upsample_interp23(hs, ratio)
if np.mod(ratio, 2)==0:
ratio = ratio + 1
pan = np.tile(pan, (1, 1, C))
pan = (pan - np.mean(pan, axis=(0, 1)))*(np.std(u_hs, axis=(0, 1), ddof=1)/np.std(pan, axis=(0, 1), ddof=1))+np.mean(u_hs, axis=(0, 1))
kernel = np.ones((ratio, ratio))
kernel = kernel/np.sum(kernel)
I_SFIM = np.zeros((M, N, C))
for i in range(C):
lrpan = signal.convolve2d(pan[:, :, i], kernel, mode='same', boundary = 'wrap')
I_SFIM[:, :, i] = u_hs[:, :, i]*pan[:, :, i]/(lrpan+1e-8)
#adjustment
I_SFIM[I_SFIM<0]=0
I_SFIM[I_SFIM>1]=1
return I_SFIM
def Wavelet(pan, hs):
M, N, c = pan.shape
m, n, C = hs.shape
ratio = int(np.round(M / m))
print('get sharpening ratio: ', ratio)
assert int(np.round(M / m)) == int(np.round(N / n))
#upsample
u_hs = upsample_interp23(hs, ratio)
pan = np.squeeze(pan)
pc = pywt.wavedec2(pan, 'haar', level=2)
rec = []
for i in range(C):
temp_dec = pywt.wavedec2(u_hs[:, :, i], 'haar', level=2)
pc[0] = temp_dec[0]
temp_rec = pywt.waverec2(pc, 'haar')
temp_rec = np.expand_dims(temp_rec, -1)
rec.append(temp_rec)
I_Wavelet = np.concatenate(rec, axis=-1)
#adjustment
I_Wavelet[I_Wavelet < 0] = 0
I_Wavelet[I_Wavelet > 1] = 1
return I_Wavelet
def PCA(pan, hs):
M, N, c = pan.shape
m, n, C = hs.shape
ratio = int(np.round(M / m))
print('get sharpening ratio: ', ratio)
assert int(np.round(M / m)) == int(np.round(N / n))
image_hr = pan
#upsample
u_hs = upsample_interp23(hs, ratio)
p = princomp(n_components=C)
pca_hs = p.fit_transform(np.reshape(u_hs, (M * N, C)))
pca_hs = np.reshape(pca_hs, (M, N, C))
I = pca_hs[:, :, 0]
image_hr = (image_hr - np.mean(image_hr)) * np.std(I, ddof=1) / np.std(
image_hr, ddof=1) + np.mean(I)
pca_hs[:, :, 0] = image_hr[:, :, 0]
I_PCA = p.inverse_transform(pca_hs)
#equalization
I_PCA = I_PCA - np.mean(I_PCA, axis=(0, 1)) + np.mean(u_hs)
#adjustment
I_PCA[I_PCA < 0] = 0
I_PCA[I_PCA > 1] = 1
return I_PCA
def MTF_GLP(pan, hs, sensor='gaussian'):
M, N, c = pan.shape
m, n, C = hs.shape
ratio = int(np.round(M/m))
print('get sharpening ratio: ', ratio)
assert int(np.round(M/m)) == int(np.round(N/n))
#upsample
u_hs = upsample_interp23(hs, ratio)
#equalization
image_hr = np.tile(pan, (1, 1, C))
image_hr = (image_hr - np.mean(image_hr, axis=(0,1)))*(np.std(u_hs, axis=(0, 1), ddof=1)/np.std(image_hr, axis=(0, 1), ddof=1))+np.mean(u_hs, axis=(0,1))
# print(image_hr.shape)
pan_lp = np.zeros_like(u_hs)
N =31
fcut = 1/ratio
match = 0
if sensor == 'gaussian':
sig = (1/(2*(2.772587)/ratio**2))**0.5
kernel = np.multiply(cv2.getGaussianKernel(9, sig), cv2.getGaussianKernel(9,sig).T)
t=[]
for i in range(C):
temp = signal.convolve2d(image_hr[:, :, i], kernel, mode='same', boundary = 'wrap')
temp = temp[0::ratio, 0::ratio]
temp = np.expand_dims(temp, -1)
t.append(temp)
t = np.concatenate(t, axis=-1)
pan_lp = upsample_interp23(t, ratio)
elif sensor == None:
match=1
GNyq = 0.3*np.ones((C,))
elif sensor=='QB':
match=1
GNyq = np.asarray([0.34, 0.32, 0.30, 0.22],dtype='float32') # Band Order: B,G,R,NIR
elif sensor=='IKONOS':
match=1 #MTF usage
GNyq = np.asarray([0.26,0.28,0.29,0.28],dtype='float32') # Band Order: B,G,R,NIR
elif sensor=='GeoEye1':
match=1 # MTF usage
GNyq = np.asarray([0.23,0.23,0.23,0.23],dtype='float32') # Band Order: B,G,R,NIR
elif sensor=='WV2':
match=1 # MTF usage
GNyq = [0.35,0.35,0.35,0.35,0.35,0.35,0.35,0.27]
elif sensor=='WV3':
match=1 #MTF usage
GNyq = 0.29 * np.ones(8)
if match==1:
t = []
for i in range(C):
alpha = np.sqrt(N*(fcut/2)**2/(-2*np.log(GNyq)))
H = np.multiply(cv2.getGaussianKernel(N, alpha[i]), cv2.getGaussianKernel(N, alpha[i]).T)
HD = H/np.max(H)
h = fir_filter_wind(HD, kaiser2d(N, 0.5))
temp = signal.convolve2d(image_hr[:, :, i], np.real(h), mode='same', boundary = 'wrap')
temp = temp[0::ratio, 0::ratio]
temp = np.expand_dims(temp, -1)
t.append(temp)
t = np.concatenate(t, axis=-1)
pan_lp = upsample_interp23(t, ratio)
I_MTF_GLP = u_hs + image_hr - pan_lp
#adjustment
I_MTF_GLP[I_MTF_GLP<0]=0
I_MTF_GLP[I_MTF_GLP>1]=1
return I_MTF_GLP
def GSA(pan, hs):
M, N, c = pan.shape
m, n, C = hs.shape
ratio = int(np.round(M / m))
print('get sharpening ratio: ', ratio)
assert int(np.round(M / m)) == int(np.round(N / n))
#upsample
u_hs = upsample_interp23(hs, ratio)
#remove means from u_hs
means = np.mean(u_hs, axis=(0, 1))
image_lr = u_hs - means
#remove means from hs
image_lr_lp = hs - np.mean(hs, axis=(0, 1))
#sintetic intensity
image_hr = pan - np.mean(pan)
image_hr0 = cv2.resize(image_hr, (n, m), cv2.INTER_CUBIC)
image_hr0 = np.expand_dims(image_hr0, -1)
alpha = estimation_alpha(image_hr0,
np.concatenate((image_lr_lp, np.ones((m, n, 1))),
axis=-1),
mode='global')
I = np.dot(np.concatenate((image_lr, np.ones((M, N, 1))), axis=-1), alpha)
I0 = I - np.mean(I)
#computing coefficients
g = []
g.append(1)
for i in range(C):
temp_h = image_lr[:, :, i]
c = np.cov(np.reshape(I0, (-1, )), np.reshape(temp_h, (-1, )), ddof=1)
g.append(c[0, 1] / np.var(I0))
g = np.array(g)
#detail extraction
delta = image_hr - I0
deltam = np.tile(delta, (1, 1, C + 1))
#fusion
V = np.concatenate((I0, image_lr), axis=-1)
g = np.expand_dims(g, 0)
g = np.expand_dims(g, 0)
g = np.tile(g, (M, N, 1))
V_hat = V + g * deltam
I_GSA = V_hat[:, :, 1:]
I_GSA = I_GSA - np.mean(I_GSA, axis=(0, 1)) + means
#adjustment
I_GSA[I_GSA < 0] = 0
I_GSA[I_GSA > 1] = 1
return I_GSA