标签: image-whitening

我想知道如何在一批图像上执行图像白化.

根据https://www.tensorflow.org/api_docs/python/tf/image/per_image_standardization中的文档,据说tf.image.per_image_standardization将形状的3D张量(即图像)作为输入[height, width, channels].

它是缺失的功能还是有不同的方法？

任何帮助深表感谢.

我正在尝试zero-center和whiten CIFAR10数据集,但我得到的结果看起来像随机噪音!
Cifar10数据集包含60,000大小的彩色图像32x32.训练集包含50,000和测试集10,000分别包含图像.
以下代码片段显示了我为使数据集变白而执行的过程:

# zero-center
mean = np.mean(data_train, axis = (0,2,3)) 
for i in range(data_train.shape[0]):
    for j in range(data_train.shape[1]):
        data_train[i,j,:,:] -= mean[j]

first_dim = data_train.shape[0] #50,000
second_dim = data_train.shape[1] * data_train.shape[2] * data_train.shape[3] # 3*32*32
shape = (first_dim, second_dim) # (50000, 3072) 

# compute the covariance matrix
cov = np.dot(data_train.reshape(shape).T, data_train.reshape(shape)) / data_train.shape[0] 
# compute the SVD factorization of the data covariance matrix
U,S,V = np.linalg.svd(cov) …