损失函数-ms-ssim和L1的混合

ms-ssim占比alpha

L1占比1-alpha

#%%

import tensorflow as tf
import numpy as np
import torch

#模仿matlab的fspecial函数，创建滤波算子（计算SSIM用）
def _tf_fspecial_gauss(size, sigma, channels=1):
    """Function to mimic the 'fspecial' gaussian MATLAB function
    """
    x_data, y_data = np.mgrid[-size//2 + 1:size//2 + 1, -size//2 + 1:size//2 + 1]

    x_data = np.expand_dims(x_data, axis=-1)
    x_data = np.expand_dims(x_data, axis=-1)

    y_data = np.expand_dims(y_data, axis=-1)
    y_data = np.expand_dims(y_data, axis=-1)

    x = tf.constant(x_data, dtype=tf.float32)
    y = tf.constant(y_data, dtype=tf.float32)

    g = tf.exp(-((x**2 + y**2)/(2.0*sigma**2)))

    window = g / tf.reduce_sum(g)
    return tf.tile(window, (1,1,channels,channels))

#高斯卷积
def tf_gauss_conv(img, filter_size=11, filter_sigma=1.5):
    _, height, width, ch = img.get_shape().as_list()
    size = min(filter_size, height, width)
    sigma = size * filter_sigma / filter_size if filter_size else 0
    window = _tf_fspecial_gauss(size, sigma, ch) # window shape [size, size]
    padded_img = tf.pad(img, [[0, 0], [size//2, size//2], [size//2, size//2], [0, 0]], mode="CONSTANT")
    return tf.nn.conv2d(padded_img, window, strides=[1,1,1,1], padding='VALID')

#高斯加权的L1正则化
def tf_gauss_weighted_l1(img1, img2, mean_metric=True, filter_size=11, filter_sigma=1.5):
    diff = tf.abs(img1 - img2)
    L1 = tf_gauss_conv(diff, filter_size=filter_size, filter_sigma=filter_sigma)
    if mean_metric:
        return tf.reduce_mean(L1)
    else:
        return L1

#计算ssim
def tf_ssim(img1, img2, cs_map=False, mean_metric=True, filter_size=11, filter_sigma=1.5):
    _, height, width, ch = img1.get_shape().as_list()
    size = min(filter_size, height, width)
    sigma = size * filter_sigma / filter_size if filter_size else 0

    window = _tf_fspecial_gauss(size, sigma, ch) # window shape [size, size]
    K1 = 0.01
    K2 = 0.03
    L = 1  # depth of image (255 in case the image has a differnt scale)
    C1 = (K1*L)**2
    C2 = (K2*L)**2

    #求取滑块内均值Ux Uy，均方值Ux_sq
    padded_img1 = tf.pad(img1, [[0, 0], [size//2, size//2], [size//2, size//2], [0, 0]], mode="CONSTANT")       #img1 上下左右补零
    padded_img2 = tf.pad(img2, [[0, 0], [size//2, size//2], [size//2, size//2], [0, 0]], mode="CONSTANT")       #img2 上下左右补零
    mu1 = tf.nn.conv2d(padded_img1, window, strides=[1,1,1,1], padding='VALID') #利用滑动窗口，求取窗口内图像的的加权平均
    mu2 = tf.nn.conv2d(padded_img2, window, strides=[1,1,1,1], padding='VALID')
    mu1_sq = mu1*mu1    #img(x,y) Ux*Ux 均方
    mu2_sq = mu2*mu2    #img(x,y) Uy*Uy
    mu1_mu2 = mu1*mu2   #img(x,y) Ux*Uy

    #求取方差，方差等于平方的期望减去期望的平方，平方的均值减去均值的平方
    paddedimg11 = padded_img1*padded_img1
    paddedimg22 = padded_img2*padded_img2
    paddedimg12 = padded_img1*padded_img2

    sigma1_sq = tf.nn.conv2d(paddedimg11, window, strides=[1,1,1,1],padding='VALID') - mu1_sq   #sigma1方差
    sigma2_sq = tf.nn.conv2d(paddedimg22, window, strides=[1,1,1,1],padding='VALID') - mu2_sq   #sigma2方差
    sigma12 = tf.nn.conv2d(paddedimg12, window, strides=[1,1,1,1],padding='VALID') - mu1_mu2    #sigma12协方差，乘积的均值减去均值的乘积

    ssim_value = tf.clip_by_value(((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2)), 0, 1)
    if cs_map:          #只考虑contrast对比度，structure结构，不考虑light亮度
        cs_map_value = tf.clip_by_value((2*sigma12 + C2)/(sigma1_sq + sigma2_sq + C2), 0, 1)    #对比度结构map
        value = (ssim_value, cs_map_value)
    else:
        value = ssim_value
    if mean_metric:             #求取矩阵的均值，否则返回ssim矩阵
        value = tf.reduce_mean(value)
    return value

#计算跨尺度结构相似度指数（通过扩大感受野方式）
def tf_ms_ssim(img1, img2, weights=None, mean_metric=False):
    if weights is None:
        weights = [1, 1, 1, 1, 1] # [0.0448, 0.2856, 0.3001, 0.2363, 0.1333] #[1, 1, 1, 1, 1] #

    level = len(weights)
    sigmas = [0.5]
    for i in range(level-1):
        sigmas.append(sigmas[-1]*2)
    weight = tf.constant(weights, dtype=tf.float32)

    mssim = []
    mcs = []
    for l, sigma in enumerate(sigmas):
        filter_size = int(max(sigma*4+1, 11))
        ssim_map, cs_map = tf_ssim(img1, img2, cs_map=True, mean_metric=False, filter_size=filter_size, filter_sigma=sigma)
        mssim.append(ssim_map)
        mcs.append(cs_map)

    # list to tensor of dim D+1
    value = mssim[level-1]**weight[level-1]
    for l in range(level):
        value = value * (mcs[l]**weight[l])
    if mean_metric:
        return tf.reduce_mean(value)
    else:
        return value

#ms-ssim和L1损失函数（SSIM占alpha,loss_L1占1-alpha）
def tf_ms_ssim_l1_loss(img1, img2, mean_metric=True, alpha=0.84):
    ms_ssim_map = tf_ms_ssim(img1, img2, mean_metric=False)
    l1_map = tf_gauss_weighted_l1(img1, img2, mean_metric=False, filter_size=33, filter_sigma=8.0)
    loss_map = (1-ms_ssim_map) * alpha + l1_map * (1-alpha)
    if mean_metric:
        return tf.reduce_mean(loss_map)
    else:
        return loss_map

#%%
img1 = np.arange(10000,dtype=np.float32).reshape([1,100,100,1])
img2 = np.arange(10000,dtype=np.float32).reshape([1,100,100,1])-1

ms_ssim_l1_loss = tf_ms_ssim_l1_loss(tf.constant(img1),tf.constant(img2))

with tf.Session() as sess:
    print(sess.run(ms_ssim_l1_loss))