自监督去噪：Noise2Noise原理及实现（Pytorch）

在这里插入图片描述

文章地址：https://arxiv.org/abs/1803.04189
ICML github 代码: https://github.com/NVlabs/noise2noise
本文整理和参考代码: https://github.com/shivamsaboo17/Deep-Restore-PyTorch

文章目录

      • 1. 理论背景
      • 2. 实验结果
      • 3. 代码实现
        • (1) 网络结构
        • (2) 数据加载
        • (3) 网络训练
        • (4) 完整流程
      • 4. 总结

文章核心句子: ‘learn to turn bad images into good images by only looking at bad images, and do this just as well， sometimes even better.’

1. 理论背景

如果有一系列观测不怎么精确的数据(y1,y2…yn)，想要得到一个可信的结果最简单的方法就是让这些观测数据的 “方差”（可以是其他度量）最小

a

r

g

m

i

n

z

E

y

{

L

(

z

,

y

)

}

underset{z}{argmin} E_y { L(z,y)}

zargmin?Ey?{L(z,y)}

不同的损失函数这里查找的最优位置不同：

  • L2 损失，

    L

    (

    z

    ,

    y

    )

    =

    (

    z

    ?

    y

    )

    2

    L(z,y) = (z-y)^2

    L(z,y)=(z?y)2的时候，最优位置是期望

    z

    =

    E

    y

    {

    y

    }

    z = E_y { y}

    z=Ey?{y}

  • L1 损失，

    L

    (

    z

    ,

    y

    )

    =

    ∣

    z

    ?

    y

    ∣

    L(z,y) = |z-y|

    L(z,y)=∣z?y∣,最优值就是中值位置

    z

    =

    m

    e

    d

    i

    a

    n

    {

    y

    }

    z = median {y }

    z=median{y}

  • L0损失，

    L

    (

    z

    ,

    y

    )

    =

    ∣

    z

    ?

    y

    ∣

    0

    L(z,y) = |z-y|_0

    L(z,y)=∣z?y∣0?, 最优值是众数，

    z

    =

    m

    o

    d

    e

    {

    y

    }

    z = mode{ y}

    z=mode{y}

将这里的z用网络进行表示

a

r

g

m

i

n

θ

E

(

x

,

y

)

{

L

(

f

θ

(

x

)

)

,

y

}

underset{ heta}{argmin} E_{(x,y)} { L(f_{ heta}(x)),y }

θargmin?E(x,y)?{L(fθ?(x)),y}

通过贝叶斯变换也等价于

a

r

g

m

i

n

θ

E

x

{

E

y

∣

x

{

L

(

f

θ

(

x

)

,

y

)

}

}

underset{ heta}{argmin} E_x { E_{y|x} { L(f_{ heta}(x), y)} }

θargmin?Ex?{Ey∣x?{L(fθ?(x),y)}}

理论上可通过优化每一个噪声图像对 (

x

i

,

y

i

x_i,y_i

xi?,yi?) 得到一个最好的拟合器

f

θ

f_{ heta}

fθ? ，但这是一个多解且不稳定的过程。比如对于一个超分辨问题来说，对于每一个输入的低分辨图像，其可能对应于多张高分辨图像，或者说多张高分辨图像的下采样可能对应同一张图像。而在高低分辨率的图像对上，使用L2损失函数训练网络，网络会学习到输出所有结果的平均值。这也是我们想要的，如果网络经过优化之后，输出的结果不是和

x

i

x_i

xi?一一对应的，而是在一个范围内的随机值，该范围的期望是

y

i

y_i

yi?。

  • 当网络还没有收敛的时候，其解空间大，方差大，得到的

    y

    i

    y_i

    yi?偏离真实结果很多

  • 而充分训练的网络，解空间变小，方差小，得到的

    y

    i

    y_i

    yi?接近真实结果

  • 解空间的大小不会随着训练的增加而无限减小，但其期望/均值总是不变的

那么上面的结论也就告诉我们，如果用一个期望和目标相匹配的随机数替换原始目标，那么其估计值是将保持不变的。也就是说如果输入条件目标分布

p

(

y

∣

x

)

p(y|x)

p(y∣x)被具有相同条件期望值的任意分布替换，最佳网络参数是保持不变的。训练的目标表示为

a

r

g

m

i

n

θ

∑

i

L

(

f

θ

(

x

i

^

)

,

y

i

^

)

underset{ heta}{argmin} sum_i L(f_{ heta}(hat{x_i}),hat{y_i})

θargmin?i∑?L(fθ?(xi?^?),yi?^?)

其中，输出和目标都是来自于有噪声的分布，其满足

E

{

y

i

^

∣

x

i

^

}

=

y

i

E{ hat{y_i} | hat{x_i} } = y_i

E{yi?^?∣xi?^?}=yi?

当给定的训练数据足够多的时候，该目标函数的解和原目标函数是相同的.当训练数据有限的时候，估计的均方误差等于目标中的噪声平方差除以训练样例数目

E

y

^

[

1

N

∑

i

y

i

?

1

N

∑

i

y

i

^

]

2

=

1

N

[

1

N

∑

i

v

a

r

(

y

i

)

]

E_{hat{y}} [frac{1}{N} sum_i y_i - frac{1}{N} sum_i hat{y_i}]^2 = frac{1}{N}[frac{1}{N} sum_i var(y_i)]

Ey^??[N1?i∑?yi??N1?i∑?yi?^?]2=N1?[N1?i∑?var(yi?)]

  • 随着样本数量的增加，误差将接近于0。
  • 即使数量有限，估计也是无偏的。

方法总结:

  1. 强行让NN学习两张 零均值噪声图片之间的映射关系
  2. 样本数量少：学习了两种零均值噪声的映射变换
  3. 样本数量多：噪声不可预测，需要最小化loss，NN倾向于输出所有可能的期望值，也就是干净图片

2. 实验结果

(1) 不同噪声：高斯噪声、poisson噪声、Bernoulli噪声

(2) 不同场景：图去文字、脉冲噪声

3. 代码实现

(1) 网络结构

SRResNet模型结构: SRGAN 图像超分辨率结构

import torch 
import torch.nn as nn
import torch.nn.functional as F

class ConvBlock(nn.Module):
    def __init__(self,input_channels,output_channels,kernel_size,stride=1,pad=1,use_act=True):
        super(ConvBlock,self).__init__()
        self.use_act = use_act
        self.conv = nn.Conv2d(input_channels,output_channels,kernel_size,stride=stride,padding=pad)
        self.bn = nn.BatchNorm2d(output_channels)
        self.act = nn.LeakyReLU(0.2,inplace=True)
    def forward(self,x):
        """
            conv2d
            batch normalization
            PReLU
        """
        op = self.bn(self.conv(x))
        if self.use_act:
            return self.act(op)
        else:
            return op 

class ResBlock(nn.Module):
    def __init__(self,input_channels,output_channels,kernel_size):
        super(ResBlock,self).__init__()
        self.block1 = ConvBlock(input_channels,output_channels,kernel_size)
        self.block2 = ConvBlock(input_channels,output_channels,kernel_size,use_act=False)
    
    def forward(self,x):
        """
            conv2d
            BN
            PReLU
            conv2d
            BN
            element sum (residule skip connection)
        """
        return x + self.block2(self.block1(x))
        
        
class SRResnet(nn.Module):
    def __init__(self,input_channels,output_channels,res_layers=16):
        super(SRResnet,self).__init__()
        self.conv1 = nn.Conv2d(input_channels,output_channels,kernel_size=3,stride=1,padding=1)
        self.act = nn.LeakyReLU(0.2,inplace=True)
        
        _resl = [ResBlock(output_channels,output_channels,3) for i in range(res_layers)]
        self.resl = nn.Sequential(*_resl)
        
        self.conv2 = ConvBlock(output_channels,output_channels,3,use_act=False)
        self.conv3 = nn.Conv2d(output_channels,input_channels,kernel_size=3,stride=1,padding=1)
    
    def forward(self,input):
        _op1 = self.act(self.conv1(input))
        _op2 = self.conv2(self.resl(_op1))
        op = self.conv3(torch.add(_op1,_op2))
        return op
    

model = SRResnet(3,64)
model

(2) 数据加载

这里用的数据是从 https://github.com/shivamsaboo17/Deep-Restore-PyTorch 下载的coco2017的数据，当然也可以从官网下载，然后将数据分为 train 和 valid两个部分。

这里准备的噪声数据有四种不同的方法，也是对应的文章中的内容

  • gaussian
  • poisson
  • multiplicative_bernoulli
  • text
from torch.utils.data import Dataset,DataLoader
import torchvision.transforms.functional as tvF
from PIL import Image,ImageFont,ImageDraw
from random import choice
from sys import platform
from random import choice
from string import ascii_letters
import numpy as np
import os 
import scipy
import cv2
import random
import matplotlib.pyplot as plt

class NoisyDataset(Dataset):
    def __init__(self, root_dir, crop_size=128, train_noise_model=('gaussian', 50), clean_targ=False):
        """
            root_dir: Path of image directory
            crop_size: Crop image to given size
            clean_targ: Use clean targets for training
        """
        self.root_dir = root_dir
        self.crop_size = crop_size
        self.clean_targ = clean_targ
        self.noise = train_noise_model[0]
        self.noise_param = train_noise_model[1]
        self.imgs = os.listdir(root_dir)
    
    def _random_crop_to_size(self, imgs):
        w, h = imgs[0].size
        assert w >= self.crop_size and h >= self.crop_size, 'Cannot be croppped. Invalid size'
        

        cropped_imgs = []
        i = np.random.randint(0, h - self.crop_size + 2)
        j = np.random.randint(0, w - self.crop_size + 2)

        for img in imgs:
            if min(w, h) < self.crop_size:
                img = tvF.resize(img, (self.crop_size, self.crop_size))
            cropped_imgs.append(tvF.crop(img, i, j, self.crop_size, self.crop_size))
        
        #cropped_imgs = cv2.resize(np.array(imgs[0]), (self.crop_size, self.crop_size))
        return cropped_imgs
    
    def _add_gaussian_noise(self, image):
        """
        Added only gaussian noise
        """
        w, h = image.size
        c = len(image.getbands())
        
        std = np.random.uniform(0, self.noise_param)
        _n = np.random.normal(0, std, (h, w, c))
        noisy_image = np.array(image) + _n
        
        noisy_image = np.clip(noisy_image, 0, 255).astype(np.uint8)
        return {'image':Image.fromarray(noisy_image), 'mask': None, 'use_mask': False}

    
    def _add_poisson_noise(self, image):
        """
            Added poisson Noise
        """
        noise_mask = np.random.poisson(np.array(image))
        #print(noise_mask.dtype)
        #print(noise_mask)
        return {'image':noise_mask.astype(np.uint8), 'mask': None, 'use_mask': False}
    
    def _add_m_bernoulli_noise(self, image):
        """
            Multiplicative bernoulli
        """
        sz = np.array(image).shape[0]
        prob_ = random.uniform(0, self.noise_param)
        mask = np.random.choice([0, 1], size=(sz, sz), p=[prob_, 1 - prob_])
        mask = np.repeat(mask[:, :, np.newaxis], 3, axis=2)
        return {'image':np.multiply(image, mask).astype(np.uint8), 'mask':mask.astype(np.uint8), 'use_mask': True}

    def _add_text_overlay(self, image):
        """
            Add text overlay to image
        """
        assert self.noise_param < 1, 'Text parameter should be probability of occupancy'

        w, h = image.size
        c = len(image.getbands())

        if platform == 'linux':
            serif = '/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf'
        else:
            serif = 'Times New Roman.ttf'

        text_img = image.copy()
        text_draw = ImageDraw.Draw(text_img)
        mask_img = Image.new('1', (w, h))
        mask_draw = ImageDraw.Draw(mask_img)

        max_occupancy = np.random.uniform(0, self.noise_param)

        def get_occupancy(x):
            y = np.array(x, np.uint8)
            return np.sum(y) / y.size

        while 1:
            font = ImageFont.truetype(serif, np.random.randint(16, 21))
            length = np.random.randint(10, 25)
            chars = ''.join(choice(ascii_letters) for i in range(length))
            color = tuple(np.random.randint(0, 255, c))
            pos = (np.random.randint(0, w), np.random.randint(0, h))
            text_draw.text(pos, chars, color, font=font)

            # Update mask and check occupancy
            mask_draw.text(pos, chars, 1, font=font)
            if get_occupancy(mask_img) > max_occupancy:
                break
        
        return {'image':text_img, 'mask':None, 'use_mask': False}
    
    def corrupt_image(self, image):
        
        if self.noise == 'gaussian':
            return self._add_gaussian_noise(image)
        elif self.noise == 'poisson':
            return self._add_poisson_noise(image)
        elif self.noise == 'multiplicative_bernoulli':
            return self._add_m_bernoulli_noise(image)
        elif self.noise == 'text':
            return self._add_text_overlay(image)
        else:
            raise ValueError('No such image corruption supported')
    
    def __getitem__(self, index):
        """
        Read a image, corrupt it and return it
        """
        img_path = os.path.join(self.root_dir, self.imgs[index])
        image = Image.open(img_path).convert('RGB')

        # 对图片进行随机切割
        if self.crop_size > 0:
            image = self._random_crop_to_size([image])[0]

        # 噪声图片1
        source_img_dict = self.corrupt_image(image)
        source_img_dict['image'] = tvF.to_tensor(source_img_dict['image'])

        if source_img_dict['use_mask']:
            source_img_dict['mask'] = tvF.to_tensor(source_img_dict['mask'])

        # 噪声图片2
        if self.clean_targ:
            #print('clean target')
            target = tvF.to_tensor(image)
        else:
            #print('corrupt target')
            _target_dict = self.corrupt_image(image)
            target = tvF.to_tensor(_target_dict['image'])
        
        image = np.array(image).astype(np.uint8)
        if source_img_dict['use_mask']:
            return [source_img_dict['image'], source_img_dict['mask'], target,image]
        else:
            return [source_img_dict['image'], target, image]

    def __len__(self):
        return len(self.imgs)

也可以对数据进行查看

data = NoisyDataset("./dataset/train/", crop_size=128) # Default gaussian noise without clean targets
dl = DataLoader(data, batch_size=1, shuffle=True)

index = 10
[img_noise1,img_noise2,img] = data.__getitem__(index)

plt.figure(figsize=(12,4))
plt.subplot(131)
plt.imshow(img)
plt.title("Clean")
plt.subplot(132)
plt.imshow(np.transpose(img_noise1,(1,2,0)))
plt.title("Noisy-1")
plt.subplot(133)
plt.imshow(np.transpose(img_noise2,(1,2,0)))
plt.title("Noisy-2")
plt.show()

在这里插入图片描述

(3) 网络训练

import torch 
import torch.nn as nn 
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F 
from torch.optim import lr_scheduler
from tqdm import tqdm
import matplotlib.pyplot as plt 
import numpy as np

class Train():
    def __init__(self,model,train_dir,val_dir,params) -> None:
        self.cuda = params['cuda']
        if self.cuda:
            self.model = model.cuda()
        else:
            self.model = model
        self.train_dir = train_dir
        self.val_dir = val_dir
        # how to add noise: gaussian/poison/ text 
        self.noise_model = params['noise_model'] 
        self.crop_size = params['crop_size']
        # pair with noise figure or clean figure
        self.clean_targs = params['clean_targs']
        self.lr = params['lr']
        self.epochs = params['epochs']
        # Wbatch size
        self.bs = params['bs']
        
        self.train_dl, self.val_dl = self.__getdataset__()
        self.optimizer = self.__getoptimizer__()
        self.scheduler = self.__getscheduler__()
        self.loss_fn = self.__getlossfn__(params['lossfn'])
    
    def __getdataset__(self):
        train_ds = NoisyDataset(self.train_dir, 
                                crop_size=self.crop_size, 
                                train_noise_model=self.noise_model,
                                clean_targ=self.clean_targs)
        train_dl = DataLoader(train_ds, 
                                batch_size=self.bs, 
                                shuffle=True)

        val_ds = NoisyDataset(self.val_dir, 
                                crop_size=self.crop_size, 
                                train_noise_model=self.noise_model,
                                clean_targ=True)
        val_dl = DataLoader(val_ds, batch_size=self.bs)
        return train_dl, val_dl

    def __getoptimizer__(self):
        
        return optim.Adam(self.model.parameters(), self.lr)

    def __getscheduler__(self):
        return lr_scheduler.ReduceLROnPlateau(self.optimizer, patience=self.epochs/4, factor=0.5, verbose=True)

    def __getlossfn__(self, lossfn):
        if lossfn == 'l2':
            return nn.MSELoss()
        elif lossfn == 'l1':
            return nn.L1Loss()
        else:
            raise ValueError('No such loss function supported')
    
    def evaluate(self):
        val_loss = 0
        self.model.eval()
        for _, valid_datalist in enumerate(self.val_dl):
            if self.cuda:
                source = valid_datalist[0].cuda()
                target = valid_datalist[-2].cuda()
            else:
                source = valid_datalist[0]
                target = valid_datalist[-2]
            _op = self.model(Variable(source))
            if len(valid_datalist) == 4:
                if self.cuda:
                    mask = Variable(valid_datalist[1].cuda())
                else:
                    mask = Variable(valid_datalist[1])
                _loss = self.loss_fn(mask * _op, mask * Variable(target))
            else:
                _loss = self.loss_fn(_op, Variable(target))
            val_loss += _loss.data
        
        return val_loss
    
    def train(self):
        pbar = tqdm(range(self.epochs))
        for i in pbar:
            tr_loss = 0
            # train mode
            self.model.train()
            for train_datalist in self.train_dl:
                # the the pair noise data
                if self.cuda:
                    source = train_datalist[0].cuda()
                    target = train_datalist[-2].cuda()
                else:
                    source = train_datalist[0]
                    target = train_datalist[-2]
                # train the nueral network
                _op = self.model(Variable(source))
                
                # if use the "multiplicative_bernoulli" just calculate the difference with the masked place
                if len(train_datalist) == 4:
                    if self.cuda:
                        mask = Variable(train_datalist[1].cuda())
                    else:
                        mask = Variable(train_datalist[1])
                    _loss = self.loss_fn(mask * _op, mask * Variable(target))
                else:
                    _loss = self.loss_fn(_op, Variable(target))
                tr_loss += _loss.data

                self.optimizer.zero_grad()
                _loss.backward()
                self.optimizer.step()
            
            val_loss = self.evaluate()
            #self.scheduler.step(val_loss)
            pbar.set_description('Train loss: {:.4f}, Val loss: {:.4f}'.format(tr_loss,val_loss))
            
            # save temp reuslt
            with torch.no_grad():
                if i%50==0:
                    source = train_datalist[0].cuda()
                    pred = self.model(Variable(source))
                    img = train_datalist[-1].cuda()
                    plt.figure(figsize=(12,4))
                    plt.subplot(131)
                    plt.imshow(torch.squeeze(img[0]).cpu().detach().numpy())
                    plt.title("Clean")
                    plt.subplot(132)
                    plt.imshow(np.transpose(torch.squeeze(source[0]).cpu().detach().numpy(),(1,2,0)))
                    plt.title("Noisy")
                    plt.subplot(133)
                    plt.imshow(np.transpose(torch.squeeze(abs(pred[0])).cpu().detach().numpy(),(1,2,0)))
                    plt.title("prediction")
                    
                    if not os.path.exists("./result/{}".format(self.noise_model[0]+"_"+str(self.noise_model[1]))):
                        os.makedirs("./result/{}".format(self.noise_model[0]+"_"+str(self.noise_model[1])))
                    plt.savefig("./result/{}/{}.png".format(self.noise_model[0]+"_"+str(self.noise_model[1]),i))
                    plt.close()

(4) 完整流程

model = SRResnet(3, 64)

params = {
    'noise_model': ('gaussian', 50),
    'crop_size': 64,
    'clean_targs': False,
    'lr': 0.001,
    'epochs': 1000,
    'bs': 32,
    'lossfn': 'l2',
    'cuda': True
}

trainer = Train(model, 'dataset/train/', 'dataset/valid/', params)

在这里插入图片描述

在这里插入图片描述

4. 总结

方法:

  1. 强行让NN学习两张 零均值噪声图片之间的映射关系
  2. 样本数量少：学习了两种零均值噪声的映射变换
  3. 样本数量多：噪声不可预测，需要最小化loss，NN倾向于输出所有可能的期望值，也就是干净图片

结果：

  1. 对于DIP、Self2Self的方法，不需要估计图像的先验信息、对噪声图像进行似然估计
  2. 对于监督学习方法，无需干净图像，只需要噪声数据对
  3. 性能有的时候回超过监督训练方法

问题：

  1. 当损失函数和噪声不匹配的时候，该方法训练的模型误差较大
  2. 均值为0的假设太强，很难进行迁移、范围性有限