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import torch
import torch.nn as nn
import torch.nn.functional as F
import functools
import kornia
from utils.utils import *
class FLOL(nn.Module):
def __init__(self, nf=64):
super(FLOL, self).__init__()
# AMPLITUDE ENHANCEMENT
self.AmpNet = nn.Sequential(
AmplitudeNet_skip(8),
nn.Sigmoid()
)
self.nf = nf
ResidualBlock_noBN_f = functools.partial(ResidualBlock_noBN, nf=nf)
self.conv_first_1 = nn.Conv2d(3 * 2, nf, 3, 1, 1, bias=True)
self.conv_first_2 = nn.Conv2d(nf, nf, 3, 2, 1, bias=True)
self.conv_first_3 = nn.Conv2d(nf, nf, 3, 2, 1, bias=True)
self.feature_extraction = make_layer(ResidualBlock_noBN_f, 1)
self.recon_trunk = make_layer(ResidualBlock_noBN_f, 1)
self.upconv1 = nn.Conv2d(nf*2, nf * 4, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(nf*2, nf * 4, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(2)
self.HRconv = nn.Conv2d(nf*2, nf, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nf, 3, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.transformer = SFNet(nf, n = 4)
self.recon_trunk_light = make_layer(ResidualBlock_noBN_f, 6)
def get_mask(self,dark): # SNR map
light = kornia.filters.gaussian_blur2d(dark, (5, 5), (1.5, 1.5))
dark = dark[:, 0:1, :, :] * 0.299 + dark[:, 1:2, :, :] * 0.587 + dark[:, 2:3, :, :] * 0.114
light = light[:, 0:1, :, :] * 0.299 + light[:, 1:2, :, :] * 0.587 + light[:, 2:3, :, :] * 0.114
noise = torch.abs(dark - light)
mask = torch.div(light, noise + 0.0001)
batch_size = mask.shape[0]
height = mask.shape[2]
width = mask.shape[3]
mask_max = torch.max(mask.view(batch_size, -1), dim=1)[0]
mask_max = mask_max.view(batch_size, 1, 1, 1)
mask_max = mask_max.repeat(1, 1, height, width)
mask = mask * 1.0 / (mask_max + 0.0001)
mask = torch.clamp(mask, min=0, max=1.0)
return mask.float()
def forward(self, x, side=False):
# AMPLITUDE ENHANCEMENT
#--------------------------------------------------------Frequency Stage---------------------------------------------------
_, _, H, W = x.shape
image_fft = torch.fft.fft2(x, norm='backward')
mag_image = torch.abs(image_fft)
pha_image = torch.angle(image_fft)
curve_amps = self.AmpNet(x)
mag_image = mag_image / (curve_amps + 0.00000001) # * d4
real_image_enhanced = mag_image * torch.cos(pha_image)
imag_image_enhanced = mag_image * torch.sin(pha_image)
img_amp_enhanced = torch.fft.ifft2(torch.complex(real_image_enhanced, imag_image_enhanced), s=(H, W),
norm='backward').real
x_center = img_amp_enhanced
rate = 2 ** 3
pad_h = (rate - H % rate) % rate
pad_w = (rate - W % rate) % rate
if pad_h != 0 or pad_w != 0:
x_center = F.pad(x_center, (0, pad_w, 0, pad_h), "reflect")
x = F.pad(x, (0, pad_w, 0, pad_h), "reflect")
#------------------------------------------Spatial Stage---------------------------------------------------------------------
L1_fea_1 = self.lrelu(self.conv_first_1(torch.cat((x_center,x),dim=1)))
L1_fea_2 = self.lrelu(self.conv_first_2(L1_fea_1)) # Encoder
L1_fea_3 = self.lrelu(self.conv_first_3(L1_fea_2))
fea = self.feature_extraction(L1_fea_3)
fea_light = self.recon_trunk_light(fea)
h_feature = fea.shape[2]
w_feature = fea.shape[3]
mask_image = self.get_mask(x_center) # SNR Map
mask = F.interpolate(mask_image, size=[h_feature, w_feature], mode='nearest') # Resize and Normalize SNR map
fea_unfold = self.transformer(fea)
channel = fea.shape[1]
mask = mask.repeat(1, channel, 1, 1)
fea = fea_unfold * (1 - mask) + fea_light * mask # SNR-based Interaction
out_noise = self.recon_trunk(fea)
out_noise = torch.cat([out_noise, L1_fea_3], dim=1)
out_noise = self.lrelu(self.pixel_shuffle(self.upconv1(out_noise)))
out_noise = torch.cat([out_noise, L1_fea_2], dim=1) # Decoder
out_noise = self.lrelu(self.pixel_shuffle(self.upconv2(out_noise)))
out_noise = torch.cat([out_noise, L1_fea_1], dim=1)
out_noise = self.lrelu(self.HRconv(out_noise))
out_noise = self.conv_last(out_noise)
out_noise = out_noise + x
out_noise = out_noise[:, :, :H, :W]
if side:
return out_noise, x_center #, mag_image, x_center, mask_image
else:
return out_noise
##############################################################################
def create_model():
net = FLOL(nf=16)
return net |