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noise_estimation.py

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+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+# Power by Zongsheng Yue 2019-01-07 14:36:55
+
+
+import numpy as np
+from cv2 import imread
+from utils import im2patch, im2double
+import time
+
+def noise_estimate(im, pch_size=8):
+    '''
+    Implement of noise level estimation of the following paper:
+    Chen G , Zhu F , Heng P A . An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference
+    on Computer Vision (ICCV). IEEE Computer Society, 2015.
+    Input:
+        im: the noise image, H x W x 3 or H x W numpy tensor, range [0,1]
+        pch_size: patch_size
+    Output:
+        noise_level: the estimated noise level
+    '''
+
+    if im.ndim == 3:
+        im = im.transpose((2, 0, 1))
+    else:
+        im = np.expand_dims(im, axis=0)
+
+    # image to patch
+    pch = im2patch(im, pch_size, 3)  # C x pch_size x pch_size x num_pch tensor
+    num_pch = pch.shape[3]
+    pch = pch.reshape((-1, num_pch))  # d x num_pch matrix
+    d = pch.shape[0]
+
+    mu = pch.mean(axis=1, keepdims=True)  # d x 1
+    X = pch - mu
+    sigma_X = np.matmul(X, X.transpose()) / num_pch
+    sig_value, _ = np.linalg.eigh(sigma_X)
+    sig_value.sort()
+
+    for ii in range(-1, -d-1, -1):
+        tau = np.mean(sig_value[:ii])
+        if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau):
+            return np.sqrt(tau)
+
+
+if __name__ == '__main__':
+    im = imread('./lena.png')
+    im = im2double(im)
+
+    noise_level = [5, 15, 20, 30, 40]
+
+    for level in noise_level:
+        sigma = level / 255
+
+        im_noise = im + np.random.randn(*im.shape) * sigma
+
+        start = time.time()
+        est_level = noise_estimate(im_noise, 8)
+        end = time.time()
+        time_elapsed = end -start
+
+        str_p = "Time: {0:.4f}, Ture Level: {1:6.4f}, Estimated Level: {2:6.4f}"
+        print(str_p.format(time_elapsed, level, est_level*255))
+
+

utils.py

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+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+# Power by Zongsheng Yue 2019-01-07 15:13:19
+
+import numpy as np
+import sys
+from math import floor
+
+def im2patch(im, pch_size, stride=1):
+    '''
+    Transform image to patches.
+    Input:
+        im: 3 x H x W or 1 X H x W image, numpy format
+        pch_size: (int, int) tuple or integer
+        stride: (int, int) tuple or integer
+    '''
+    if isinstance(pch_size, tuple):
+        pch_H, pch_W = pch_size
+    elif isinstance(pch_size, int):
+        pch_H = pch_W = pch_size
+    else:
+        sys.exit('The input of pch_size must be a integer or a int tuple!')
+
+    if isinstance(stride, tuple):
+        stride_H, stride_W = stride
+    elif isinstance(stride, int):
+        stride_H = stride_W = stride
+    else:
+        sys.exit('The input of stride must be a integer or a int tuple!')
+
+
+    C, H, W = im.shape
+    num_H = len(range(0, H-pch_H+1, stride_H))
+    num_W = len(range(0, W-pch_W+1, stride_W))
+    num_pch = num_H * num_W
+    pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype)
+    kk = 0
+    for ii in range(pch_H):
+        for jj in range(pch_W):
+            temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W]
+            pch[:, kk, :] = temp.reshape((C, num_pch))
+            kk += 1
+
+    return pch.reshape((C, pch_H, pch_W, num_pch))
+
+def im2double(im):
+    '''
+    Input:
+        im: numpy uint format image, RGB or Gray, 
+    '''
+
+    im = im.astype(np.float)
+    min_val = np.min(im.ravel())
+    max_val = np.max(im.ravel())
+
+    out = (im - min_val) / (max_val - min_val)
+
+    return out