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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import scipy.stats as st
from utils import pair_downsampler,calculate_local_variance,LocalMean
EPS = 1e-9
PI = 22.0 / 7.0
class LossFunction(nn.Module):
def __init__(self):
super(LossFunction, self).__init__()
self._l2_loss = nn.MSELoss()
self._l1_loss = nn.L1Loss()
self.smooth_loss = SmoothLoss()
self.texture_difference=TextureDifference()
self.local_mean=LocalMean(patch_size=5)
self.L_TV_loss=L_TV()
def forward(self,input,L_pred1,L_pred2,L2,s2,s21,s22,H2,H11,H12,H13,s13,H14,s14,H3,s3,H3_pred,H4_pred,L_pred1_L_pred2_diff,H3_denoised1_H3_denoised2_diff,H2_blur,H3_blur):
eps = 1e-9
input = input + eps
input_Y = L2.detach()[:, 2, :, :] * 0.299 + L2.detach()[:, 1, :, :] * 0.587 + L2.detach()[:, 0, :, :] * 0.144
input_Y_mean = torch.mean(input_Y, dim=(1, 2))
enhancement_factor = 0.5/ (input_Y_mean + eps)
enhancement_factor = enhancement_factor.unsqueeze(1).unsqueeze(2).unsqueeze(3)
enhancement_factor = torch.clamp(enhancement_factor, 1, 25)
adjustment_ratio = torch.pow(0.7, -enhancement_factor) / enhancement_factor
adjustment_ratio = adjustment_ratio.repeat(1, 3, 1, 1)
normalized_low_light_layer = L2.detach() / s2
normalized_low_light_layer = torch.clamp(normalized_low_light_layer, eps, 0.8)
enhanced_brightness=torch.pow(L2.detach()*enhancement_factor, enhancement_factor)
clamped_enhanced_brightness = torch.clamp(enhanced_brightness * adjustment_ratio, eps, 1)
clamped_adjusted_low_light = torch.clamp(L2.detach() * enhancement_factor,eps,1)
loss = 0
#Enhance_loss
loss += self._l2_loss(s2, clamped_enhanced_brightness) *700
loss += self._l2_loss(normalized_low_light_layer, clamped_adjusted_low_light) *1000
loss += self.smooth_loss(L2.detach(), s2) *5
loss += self.L_TV_loss(s2)*1600
#Loss_res_1
L11, L12 = pair_downsampler(input)
loss += self._l2_loss(L11, L_pred2) * 1000
loss += self._l2_loss(L12, L_pred1) * 1000
denoised1, denoised2 = pair_downsampler(L2)
loss += self._l2_loss(L_pred1, denoised1) * 1000
loss += self._l2_loss(L_pred2, denoised2) * 1000
# Loss_res_2
loss += self._l2_loss(H3_pred, torch.cat([H12.detach(), s22.detach()], 1)) * 1000
loss += self._l2_loss(H4_pred, torch.cat([H11.detach(), s21.detach()], 1)) * 1000
H3_denoised1, H3_denoised2 = pair_downsampler(H3)
loss += self._l2_loss(H3_pred[:, 0:3, :, :], H3_denoised1) * 1000
loss += self._l2_loss(H4_pred[:, 0:3, :, :], H3_denoised2) * 1000
#Loss_color
loss += self._l2_loss(H2_blur.detach(), H3_blur) * 10000
#Loss_ill
loss += self._l2_loss(s2.detach(), s3) * 1000
#Loss_cons
local_mean1 = self.local_mean(H3_denoised1)
local_mean2 = self.local_mean(H3_denoised2)
weighted_diff1 = (1 - H3_denoised1_H3_denoised2_diff) * local_mean1+H3_denoised1*H3_denoised1_H3_denoised2_diff
weighted_diff2 = (1 - H3_denoised1_H3_denoised2_diff) * local_mean2+H3_denoised1*H3_denoised1_H3_denoised2_diff
loss += self._l2_loss(H3_denoised1,weighted_diff1)* 10000
loss += self._l2_loss(H3_denoised2, weighted_diff2)* 10000
#Loss_Var
noise_std = calculate_local_variance(H3 - H2)
H2_var = calculate_local_variance(H2)
loss += self._l2_loss(H2_var, noise_std) * 1000
return loss
def local_mean(self, image):
padding = self.patch_size // 2
image = F.pad(image, (padding, padding, padding, padding), mode='reflect')
patches = image.unfold(2, self.patch_size, 1).unfold(3, self.patch_size, 1)
return patches.mean(dim=(4, 5))
def gauss_kernel(kernlen=21, nsig=3, channels=1):
interval = (2 * nsig + 1.) / (kernlen)
x = np.linspace(-nsig - interval / 2., nsig + interval / 2., kernlen + 1)
kern1d = np.diff(st.norm.cdf(x))
kernel_raw = np.sqrt(np.outer(kern1d, kern1d))
kernel = kernel_raw / kernel_raw.sum()
out_filter = np.array(kernel, dtype=np.float32)
out_filter = out_filter.reshape((kernlen, kernlen, 1, 1))
out_filter = np.repeat(out_filter, channels, axis=2)
return out_filter
class TextureDifference(nn.Module):
def __init__(self, patch_size=5, constant_C=1e-5,threshold=0.975):
super(TextureDifference, self).__init__()
self.patch_size = patch_size
self.constant_C = constant_C
self.threshold = threshold
def forward(self, image1, image2):
# Convert RGB images to grayscale
image1 = self.rgb_to_gray(image1)
image2 = self.rgb_to_gray(image2)
stddev1 = self.local_stddev(image1)
stddev2 = self.local_stddev(image2)
numerator = 2 * stddev1 * stddev2
denominator = stddev1 ** 2 + stddev2 ** 2 + self.constant_C
diff = numerator / denominator
# Apply threshold to diff tensor
binary_diff = torch.where(diff > self.threshold, torch.tensor(1.0, device=diff.device),
torch.tensor(0.0, device=diff.device))
return binary_diff
def local_stddev(self, image):
padding = self.patch_size // 2
image = F.pad(image, (padding, padding, padding, padding), mode='reflect')
patches = image.unfold(2, self.patch_size, 1).unfold(3, self.patch_size, 1)
mean = patches.mean(dim=(4, 5), keepdim=True)
squared_diff = (patches - mean) ** 2
local_variance = squared_diff.mean(dim=(4, 5))
local_stddev = torch.sqrt(local_variance+1e-9)
return local_stddev
def rgb_to_gray(self, image):
# Convert RGB image to grayscale using the luminance formula
gray_image = 0.144 * image[:, 0, :, :] + 0.5870 * image[:, 1, :, :] + 0.299 * image[:, 2, :, :]
return gray_image.unsqueeze(1) # Add a channel dimension for compatibility
class L_TV(nn.Module):
def __init__(self,TVLoss_weight=1):
super(L_TV,self).__init__()
self.TVLoss_weight = TVLoss_weight
def forward(self,x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = (x.size()[2]-1) * x.size()[3]
count_w = x.size()[2] * (x.size()[3] - 1)
h_tv = torch.pow((x[:,:,1:,:]-x[:,:,:h_x-1,:]),2).sum()
w_tv = torch.pow((x[:,:,:,1:]-x[:,:,:,:w_x-1]),2).sum()
return self.TVLoss_weight*2*(h_tv/count_h+w_tv/count_w)/batch_size
class Blur(nn.Module):
def __init__(self, nc):
super(Blur, self).__init__()
self.nc = nc
kernel = gauss_kernel(kernlen=21, nsig=3, channels=self.nc)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
kernel = torch.from_numpy(kernel).permute(2, 3, 0, 1).to(device)
self.weight = nn.Parameter(data=kernel, requires_grad=False).to(device)
def forward(self, x):
if x.size(1) != self.nc:
raise RuntimeError(
"The channel of input [%d] does not match the preset channel [%d]" % (x.size(1), self.nc))
x = F.conv2d(x, self.weight, stride=1, padding=10, groups=self.nc)
return x
class SmoothLoss(nn.Module):
def __init__(self):
super(SmoothLoss, self).__init__()
self.sigma = 10
def rgb2yCbCr(self, input_im):
im_flat = input_im.contiguous().view(-1, 3).float()
# [w,h,3] => [w*h,3]
device = input_im.device # Use same device as input
mat = torch.Tensor([[0.257, -0.148, 0.439], [0.564, -0.291, -0.368], [0.098, 0.439, -0.071]]).to(device)
# [3,3]
bias = torch.Tensor([16.0 / 255.0, 128.0 / 255.0, 128.0 / 255.0]).to(device)
# [1,3]
temp = im_flat.mm(mat) + bias
# [w*h,3]*[3,3]+[1,3] => [w*h,3]
out = temp.view(input_im.shape[0], 3, input_im.shape[2], input_im.shape[3])
return out
# output: output input:input
def forward(self, input, output):
self.output = output
self.input = self.rgb2yCbCr(input)
sigma_color = -1.0 / (2 * self.sigma * self.sigma)
w1 = torch.exp(torch.sum(torch.pow(self.input[:, :, 1:, :] - self.input[:, :, :-1, :], 2), dim=1,
keepdim=True) * sigma_color)
w2 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-1, :] - self.input[:, :, 1:, :], 2), dim=1,
keepdim=True) * sigma_color)
w3 = torch.exp(torch.sum(torch.pow(self.input[:, :, :, 1:] - self.input[:, :, :, :-1], 2), dim=1,
keepdim=True) * sigma_color)
w4 = torch.exp(torch.sum(torch.pow(self.input[:, :, :, :-1] - self.input[:, :, :, 1:], 2), dim=1,
keepdim=True) * sigma_color)
w5 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-1, :-1] - self.input[:, :, 1:, 1:], 2), dim=1,
keepdim=True) * sigma_color)
w6 = torch.exp(torch.sum(torch.pow(self.input[:, :, 1:, 1:] - self.input[:, :, :-1, :-1], 2), dim=1,
keepdim=True) * sigma_color)
w7 = torch.exp(torch.sum(torch.pow(self.input[:, :, 1:, :-1] - self.input[:, :, :-1, 1:], 2), dim=1,
keepdim=True) * sigma_color)
w8 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-1, 1:] - self.input[:, :, 1:, :-1], 2), dim=1,
keepdim=True) * sigma_color)
w9 = torch.exp(torch.sum(torch.pow(self.input[:, :, 2:, :] - self.input[:, :, :-2, :], 2), dim=1,
keepdim=True) * sigma_color)
w10 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-2, :] - self.input[:, :, 2:, :], 2), dim=1,
keepdim=True) * sigma_color)
w11 = torch.exp(torch.sum(torch.pow(self.input[:, :, :, 2:] - self.input[:, :, :, :-2], 2), dim=1,
keepdim=True) * sigma_color)
w12 = torch.exp(torch.sum(torch.pow(self.input[:, :, :, :-2] - self.input[:, :, :, 2:], 2), dim=1,
keepdim=True) * sigma_color)
w13 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-2, :-1] - self.input[:, :, 2:, 1:], 2), dim=1,
keepdim=True) * sigma_color)
w14 = torch.exp(torch.sum(torch.pow(self.input[:, :, 2:, 1:] - self.input[:, :, :-2, :-1], 2), dim=1,
keepdim=True) * sigma_color)
w15 = torch.exp(torch.sum(torch.pow(self.input[:, :, 2:, :-1] - self.input[:, :, :-2, 1:], 2), dim=1,
keepdim=True) * sigma_color)
w16 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-2, 1:] - self.input[:, :, 2:, :-1], 2), dim=1,
keepdim=True) * sigma_color)
w17 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-1, :-2] - self.input[:, :, 1:, 2:], 2), dim=1,
keepdim=True) * sigma_color)
w18 = torch.exp(torch.sum(torch.pow(self.input[:, :, 1:, 2:] - self.input[:, :, :-1, :-2], 2), dim=1,
keepdim=True) * sigma_color)
w19 = torch.exp(torch.sum(torch.pow(self.input[:, :, 1:, :-2] - self.input[:, :, :-1, 2:], 2), dim=1,
keepdim=True) * sigma_color)
w20 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-1, 2:] - self.input[:, :, 1:, :-2], 2), dim=1,
keepdim=True) * sigma_color)
w21 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-2, :-2] - self.input[:, :, 2:, 2:], 2), dim=1,
keepdim=True) * sigma_color)
w22 = torch.exp(torch.sum(torch.pow(self.input[:, :, 2:, 2:] - self.input[:, :, :-2, :-2], 2), dim=1,
keepdim=True) * sigma_color)
w23 = torch.exp(torch.sum(torch.pow(self.input[:, :, 2:, :-2] - self.input[:, :, :-2, 2:], 2), dim=1,
keepdim=True) * sigma_color)
w24 = torch.exp(torch.sum(torch.pow(self.input[:, :, :-2, 2:] - self.input[:, :, 2:, :-2], 2), dim=1,
keepdim=True) * sigma_color)
p = 1.0
pixel_grad1 = w1 * torch.norm((self.output[:, :, 1:, :] - self.output[:, :, :-1, :]), p, dim=1, keepdim=True)
pixel_grad2 = w2 * torch.norm((self.output[:, :, :-1, :] - self.output[:, :, 1:, :]), p, dim=1, keepdim=True)
pixel_grad3 = w3 * torch.norm((self.output[:, :, :, 1:] - self.output[:, :, :, :-1]), p, dim=1, keepdim=True)
pixel_grad4 = w4 * torch.norm((self.output[:, :, :, :-1] - self.output[:, :, :, 1:]), p, dim=1, keepdim=True)
pixel_grad5 = w5 * torch.norm((self.output[:, :, :-1, :-1] - self.output[:, :, 1:, 1:]), p, dim=1, keepdim=True)
pixel_grad6 = w6 * torch.norm((self.output[:, :, 1:, 1:] - self.output[:, :, :-1, :-1]), p, dim=1, keepdim=True)
pixel_grad7 = w7 * torch.norm((self.output[:, :, 1:, :-1] - self.output[:, :, :-1, 1:]), p, dim=1, keepdim=True)
pixel_grad8 = w8 * torch.norm((self.output[:, :, :-1, 1:] - self.output[:, :, 1:, :-1]), p, dim=1, keepdim=True)
pixel_grad9 = w9 * torch.norm((self.output[:, :, 2:, :] - self.output[:, :, :-2, :]), p, dim=1, keepdim=True)
pixel_grad10 = w10 * torch.norm((self.output[:, :, :-2, :] - self.output[:, :, 2:, :]), p, dim=1, keepdim=True)
pixel_grad11 = w11 * torch.norm((self.output[:, :, :, 2:] - self.output[:, :, :, :-2]), p, dim=1, keepdim=True)
pixel_grad12 = w12 * torch.norm((self.output[:, :, :, :-2] - self.output[:, :, :, 2:]), p, dim=1, keepdim=True)
pixel_grad13 = w13 * torch.norm((self.output[:, :, :-2, :-1] - self.output[:, :, 2:, 1:]), p, dim=1,
keepdim=True)
pixel_grad14 = w14 * torch.norm((self.output[:, :, 2:, 1:] - self.output[:, :, :-2, :-1]), p, dim=1,
keepdim=True)
pixel_grad15 = w15 * torch.norm((self.output[:, :, 2:, :-1] - self.output[:, :, :-2, 1:]), p, dim=1,
keepdim=True)
pixel_grad16 = w16 * torch.norm((self.output[:, :, :-2, 1:] - self.output[:, :, 2:, :-1]), p, dim=1,
keepdim=True)
pixel_grad17 = w17 * torch.norm((self.output[:, :, :-1, :-2] - self.output[:, :, 1:, 2:]), p, dim=1,
keepdim=True)
pixel_grad18 = w18 * torch.norm((self.output[:, :, 1:, 2:] - self.output[:, :, :-1, :-2]), p, dim=1,
keepdim=True)
pixel_grad19 = w19 * torch.norm((self.output[:, :, 1:, :-2] - self.output[:, :, :-1, 2:]), p, dim=1,
keepdim=True)
pixel_grad20 = w20 * torch.norm((self.output[:, :, :-1, 2:] - self.output[:, :, 1:, :-2]), p, dim=1,
keepdim=True)
pixel_grad21 = w21 * torch.norm((self.output[:, :, :-2, :-2] - self.output[:, :, 2:, 2:]), p, dim=1,
keepdim=True)
pixel_grad22 = w22 * torch.norm((self.output[:, :, 2:, 2:] - self.output[:, :, :-2, :-2]), p, dim=1,
keepdim=True)
pixel_grad23 = w23 * torch.norm((self.output[:, :, 2:, :-2] - self.output[:, :, :-2, 2:]), p, dim=1,
keepdim=True)
pixel_grad24 = w24 * torch.norm((self.output[:, :, :-2, 2:] - self.output[:, :, 2:, :-2]), p, dim=1,
keepdim=True)
ReguTerm1 = torch.mean(pixel_grad1) \
+ torch.mean(pixel_grad2) \
+ torch.mean(pixel_grad3) \
+ torch.mean(pixel_grad4) \
+ torch.mean(pixel_grad5) \
+ torch.mean(pixel_grad6) \
+ torch.mean(pixel_grad7) \
+ torch.mean(pixel_grad8) \
+ torch.mean(pixel_grad9) \
+ torch.mean(pixel_grad10) \
+ torch.mean(pixel_grad11) \
+ torch.mean(pixel_grad12) \
+ torch.mean(pixel_grad13) \
+ torch.mean(pixel_grad14) \
+ torch.mean(pixel_grad15) \
+ torch.mean(pixel_grad16) \
+ torch.mean(pixel_grad17) \
+ torch.mean(pixel_grad18) \
+ torch.mean(pixel_grad19) \
+ torch.mean(pixel_grad20) \
+ torch.mean(pixel_grad21) \
+ torch.mean(pixel_grad22) \
+ torch.mean(pixel_grad23) \
+ torch.mean(pixel_grad24)
total_term = ReguTerm1
return total_term
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