├── figure ├── Y_net.png └── example.png ├── model └── model_last.pkl ├── haze_img ├── 1421_1_0.12.jpg ├── 1403_0.85_0.08.jpg └── 1420_0.85_0.08.jpg ├── wavelet_ssim_loss.py ├── README.md ├── dwt.py ├── demo.py ├── vertical_net.py ├── pytorch_ssim └── __init__.py └── LICENSE /figure/Y_net.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dectrfov/Y-net/HEAD/figure/Y_net.png -------------------------------------------------------------------------------- /figure/example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dectrfov/Y-net/HEAD/figure/example.png -------------------------------------------------------------------------------- /model/model_last.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dectrfov/Y-net/HEAD/model/model_last.pkl -------------------------------------------------------------------------------- /haze_img/1421_1_0.12.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dectrfov/Y-net/HEAD/haze_img/1421_1_0.12.jpg -------------------------------------------------------------------------------- /haze_img/1403_0.85_0.08.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dectrfov/Y-net/HEAD/haze_img/1403_0.85_0.08.jpg -------------------------------------------------------------------------------- /haze_img/1420_0.85_0.08.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dectrfov/Y-net/HEAD/haze_img/1420_0.85_0.08.jpg -------------------------------------------------------------------------------- /wavelet_ssim_loss.py: -------------------------------------------------------------------------------- 1 | import pytorch_ssim 2 | import torch.nn as nn 3 | from dwt import dwt 4 | 5 | ssim_loss = pytorch_ssim.SSIM() 6 | 7 | 8 | class WSloss(nn.Module): 9 | def __init__(self): 10 | super(WSloss, self).__init__() 11 | 12 | def forward(self, x, y, r=0.7): 13 | loss = 0 14 | loss -= ssim_loss(x, y) 15 | l, m, h = 1, 1, 1 16 | for i in range(2): 17 | l, m, h = l * r * r, l * r * (1 - r), l * (1 - r) * (1 - r) 18 | x0, x1, x2 = dwt(x) 19 | y0, y1, y2 = dwt(y) 20 | loss = loss - ssim_loss(x1, y1) * 2 * m - ssim_loss(x2, y2) * h 21 | x, y = x0, y0 22 | loss -= ssim_loss(x0, y0) * l 23 | return loss 24 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Y-net 2 | Y-NET: MULTI-SCALE FEATURE AGGREGATION NETWORK WITH WAVELET STRUCTURE SIMILARITY LOSS FUNCTION FOR SINGLE IMAGE DEHAZING - ICASSP 2020 3 | 4 | This repository shows implementation of [Y-net and Wavelet Structure Simlarity Loss Function](https://ieeexplore.ieee.org/document/9053920). 5 | 6 | We provide the loss function and the Y-net. Please refer our previous [repository](https://github.com/dectrfov/Wavelet-U-net-Dehazing) that contains wholes training codes. 7 | 8 |

9 | 10 | ## Dependencies 11 | * [Python 3.6+](https://www.continuum.io/downloads) 12 | * [PyTorch 1.1.0+](http://pytorch.org/) 13 | * [PyWt](https://pypi.org/project/PyWt/) 14 | 15 | ## Usage 16 | ### 1. Cloning the repository 17 | ```bash 18 | $ git clone https://github.com/dectrfov/Y-net.git 19 | $ cd Y-net 20 | ``` 21 | ### 2. Testing 22 | ```bash 23 | $ python demo.py -d haze_img -m model/model_last.pkl 24 | ``` 25 | ### 3. Use the Wavelet Structure Simlarity Loss Function 26 | ```bash 27 | from wavelet_ssim_loss import WSloss 28 | loss = WSloss() 29 | ``` 30 |

31 | -------------------------------------------------------------------------------- /dwt.py: -------------------------------------------------------------------------------- 1 | import pywt 2 | import torch 3 | import torch.nn.functional as F 4 | from torch.autograd import Variable 5 | 6 | w = pywt.Wavelet('db1') 7 | 8 | dec_hi = torch.Tensor(w.dec_hi[::-1]) 9 | dec_lo = torch.Tensor(w.dec_lo[::-1]) 10 | rec_hi = torch.Tensor(w.rec_hi) 11 | rec_lo = torch.Tensor(w.rec_lo) 12 | 13 | Lfilters = torch.stack([dec_lo.unsqueeze(0) * dec_lo.unsqueeze(1)], dim=0) 14 | Mfilters = torch.stack([ 15 | dec_lo.unsqueeze(0) * dec_hi.unsqueeze(1), 16 | dec_hi.unsqueeze(0) * dec_lo.unsqueeze(1) 17 | ], 18 | dim=0) 19 | Hfilters = torch.stack([dec_hi.unsqueeze(0) * dec_hi.unsqueeze(1)], dim=0) 20 | 21 | 22 | def dwt(img): 23 | Lfilters_cat = torch.cat(tuple(Lfilters[:, None]) * img.shape[1], 0) 24 | Lfilters_cat = Lfilters_cat.unsqueeze(1) 25 | Mfilters_cat = torch.cat(tuple(Mfilters[:, None]) * img.shape[1], 0) 26 | Mfilters_cat = Mfilters_cat.unsqueeze(1) 27 | Hfilters_cat = torch.cat(tuple(Hfilters[:, None]) * img.shape[1], 0) 28 | Hfilters_cat = Hfilters_cat.unsqueeze(1) 29 | return F.conv2d(img, Variable(Lfilters_cat.cuda(),requires_grad=True),stride=2, groups=img.shape[1]) \ 30 | ,F.conv2d(img, Variable(Mfilters_cat.cuda(),requires_grad=True),stride=2, groups=img.shape[1]) \ 31 | ,F.conv2d(img, Variable(Hfilters_cat.cuda(),requires_grad=True),stride=2, groups=img.shape[1]) 32 | -------------------------------------------------------------------------------- /demo.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.nn 4 | import torchvision 5 | from PIL import Image 6 | import argparse 7 | from vertical_net import vertical_net 8 | 9 | 10 | def get_config(): 11 | 12 | parser = argparse.ArgumentParser() 13 | parser.add_argument('-d', 14 | '--data_path', 15 | type=str, 16 | help='Testing image path') 17 | parser.add_argument('-m', '--model', type=str, help='dehazing model') 18 | config, unparsed = parser.parse_known_args() 19 | return config, unparsed 20 | 21 | 22 | def make_test_data(img_path_list, device): 23 | data_transform = torchvision.transforms.Compose( 24 | [torchvision.transforms.ToTensor()]) 25 | imgs = [] 26 | for img_path in img_path_list: 27 | x = data_transform(Image.open(str(img_path))).unsqueeze(0) 28 | x = x.to(device) 29 | imgs.append(x) 30 | return imgs 31 | 32 | 33 | def load_pretrain_network(cfg, device): 34 | network = vertical_net().to(device) 35 | weight = torch.load(cfg.model) 36 | if ('state_dict' in weight): 37 | network.load_state_dict(weight['state_dict']) 38 | else: 39 | network.load_state_dict(weight) 40 | return network 41 | 42 | 43 | def main(cfg): 44 | # ------------------------------------------------------------------- 45 | # basic config 46 | 47 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 48 | path = cfg.data_path 49 | # ------------------------------------------------------------------- 50 | # load network 51 | network = load_pretrain_network(cfg, device) 52 | 53 | imgs = os.listdir(path) 54 | print('Start eval') 55 | network.eval() 56 | 57 | for img in imgs: 58 | 59 | test_file_path = os.path.join(path, img) 60 | 61 | test_images = make_test_data([test_file_path], device) 62 | dehaze_image = network(test_images[0]) 63 | dehaze_image = dehaze_image.to('cpu') 64 | torchvision.utils.save_image(dehaze_image, 65 | os.path.join(path, 'clear_' + img)) 66 | 67 | 68 | if __name__ == '__main__': 69 | 70 | config_args, unparsed_args = get_config() 71 | main(config_args) 72 | -------------------------------------------------------------------------------- /vertical_net.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | 5 | class vertical_net(nn.Module): 6 | def __init__(self): 7 | super(vertical_net, self).__init__() 8 | 9 | self.relu = nn.ReLU() 10 | self.conv1 = nn.Conv2d(3, 16, 3, 2, 1, bias=True) 11 | self.conv2 = nn.Conv2d(16, 32, 3, 2, 1, bias=True) 12 | self.conv3 = nn.Conv2d(32, 64, 3, 2, 1, bias=True) 13 | self.conv4 = nn.Conv2d(64, 128, 3, 2, 1, bias=True) 14 | self.conv5 = nn.Conv2d(358, 3, 1, 1, 0, bias=False) 15 | self.up = nn.Upsample(scale_factor=2, mode='bilinear') 16 | 17 | self.dconv1 = nn.Conv2d(128, 64, 3, 1, 1, bias=True) 18 | self.dconv2 = nn.Conv2d(64, 32, 3, 1, 1, bias=True) 19 | self.dconv3 = nn.Conv2d(32, 16, 3, 1, 1, bias=True) 20 | self.dconv4 = nn.Conv2d(16, 3, 3, 1, 1, bias=True) 21 | 22 | self.cconv1 = nn.Conv2d(128, 3, 1, 1, 0, bias=False) 23 | self.cconv2 = nn.Conv2d(64, 3, 1, 1, 0, bias=False) 24 | self.cconv3 = nn.Conv2d(32, 3, 1, 1, 0, bias=False) 25 | self.cconv4 = nn.Conv2d(6, 3, 1, 1, 0, bias=False) 26 | self.cconv5 = nn.Conv2d(15, 3, 1, 1, 0, bias=False) 27 | 28 | def forward(self, x): 29 | x1 = self.relu(self.conv1(x)) 30 | x2 = self.relu(self.conv2(x1)) 31 | x3 = self.relu(self.conv3(x2)) 32 | x4 = self.relu(self.conv4(x3)) 33 | x5 = nn.Upsample(scale_factor=2, mode='bilinear')(x4) 34 | x5 = self.relu(self.dconv1(x5)) 35 | x6 = nn.Upsample(scale_factor=2, mode='bilinear')(x5) 36 | x6 = self.relu(self.dconv2(x6)) 37 | x7 = nn.Upsample(scale_factor=2, mode='bilinear')(x6) 38 | x7 = self.relu(self.dconv3(x7)) 39 | x8 = nn.Upsample(scale_factor=2, mode='bilinear')(x7) 40 | x8 = self.relu(self.dconv4(x8)) 41 | 42 | # merge different feature 43 | x9 = torch.cat([x3, x5], 1) 44 | x9 = self.relu(self.cconv1(x9)) 45 | x9 = nn.Upsample(scale_factor=8, mode='bilinear')(x9) 46 | 47 | x10 = torch.cat([x2, x6], 1) 48 | x10 = nn.Upsample(scale_factor=4, mode='bilinear')(x10) 49 | x10 = self.relu(self.cconv2(x10)) 50 | 51 | x11 = torch.cat([x1, x7], 1) 52 | x11 = nn.Upsample(scale_factor=2, mode='bilinear')(x11) 53 | x11 = self.relu(self.cconv3(x11)) 54 | 55 | x12 = torch.cat([x9, x10, x11, x, x8], 1) 56 | y = self.relu(self.cconv5(x12)) 57 | 58 | return y 59 | -------------------------------------------------------------------------------- /pytorch_ssim/__init__.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from torch.autograd import Variable 4 | import numpy as np 5 | from math import exp 6 | 7 | def gaussian(window_size, sigma): 8 | gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)]) 9 | return gauss/gauss.sum() 10 | 11 | def create_window(window_size, channel): 12 | _1D_window = gaussian(window_size, 1.5).unsqueeze(1) 13 | _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) 14 | window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) 15 | return window 16 | 17 | def _ssim(img1, img2, window, window_size, channel, size_average = True): 18 | mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel) 19 | mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel) 20 | 21 | mu1_sq = mu1.pow(2) 22 | mu2_sq = mu2.pow(2) 23 | mu1_mu2 = mu1*mu2 24 | 25 | sigma1_sq = F.conv2d(img1*img1, window, padding = window_size//2, groups = channel) - mu1_sq 26 | sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = channel) - mu2_sq 27 | sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = channel) - mu1_mu2 28 | 29 | C1 = 0.01**2 30 | C2 = 0.03**2 31 | 32 | ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2)) 33 | 34 | if size_average: 35 | return ssim_map.mean() 36 | else: 37 | return ssim_map.mean(1).mean(1).mean(1) 38 | 39 | class SSIM(torch.nn.Module): 40 | def __init__(self, window_size = 11, size_average = True): 41 | super(SSIM, self).__init__() 42 | self.window_size = window_size 43 | self.size_average = size_average 44 | self.channel = 1 45 | self.window = create_window(window_size, self.channel) 46 | 47 | def forward(self, img1, img2): 48 | (_, channel, _, _) = img1.size() 49 | 50 | if channel == self.channel and self.window.data.type() == img1.data.type(): 51 | window = self.window 52 | else: 53 | window = create_window(self.window_size, channel) 54 | 55 | if img1.is_cuda: 56 | window = window.cuda(img1.get_device()) 57 | window = window.type_as(img1) 58 | 59 | self.window = window 60 | self.channel = channel 61 | 62 | 63 | return _ssim(img1, img2, window, self.window_size, channel, self.size_average) 64 | 65 | def ssim(img1, img2, window_size = 11, size_average = True): 66 | (_, channel, _, _) = img1.size() 67 | window = create_window(window_size, channel) 68 | 69 | if img1.is_cuda: 70 | window = window.cuda(img1.get_device()) 71 | window = window.type_as(img1) 72 | 73 | return _ssim(img1, img2, window, window_size, channel, size_average) 74 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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