文章目录

• 1. 原理概述
• 2. 实验环节
• • 2.1 验证与opencv 库函数的结果一致
  • 2.2 与 双边滤波比较
  • 2.3 引导滤波应用，fathering
  • 2.3 引导滤波应用，图像增强
  • 2.4 灰度图引导，和各自通道引导的效果差异
  • 2.5 不同参数设置影响
• 3. 参考

1. 原理概述

引导滤波是三大保边平滑算法之一。
原理介绍参考 图像处理基础（一）引导滤波

2. 实验环节

2.1 验证与opencv 库函数的结果一致

1. 引导图是单通道时的函数guided_filter(I,p,win_size,eps)
2. 引导图时三通道时的函数multi_dim_guide_filter(I, p, r, eps)
3. I, p的输入如果归一化 0-1之间，则eps设置为小于1的数，比如0.2，
   如果没有归一化，则 eps 需要乘以 (255 * 255)
4. I, p应该是浮点数
5. cv2.ximgproc.guidedFilter 的输入参数r是 window_size // 2

实验图像

guided_filter 和multi_dim_guide_filter 代码：

import cv2
import numpy as np
from matplotlib import pyplot as pltdef guided_filter(I,p,win_size,eps):'''%   - guidance image: I (should be a gray-scale/single channel image)%   - filtering input image: p (should be a gray-scale/single channel image)%   - local window radius: r%   - regularization parameter: eps'''mean_I = cv2.blur(I,(win_size,win_size))mean_p = cv2.blur(p,(win_size,win_size))mean_II = cv2.blur(I*I,(win_size,win_size))mean_Ip = cv2.blur(I*p,(win_size,win_size))var_I = mean_II - mean_I*mean_Icov_Ip = mean_Ip - mean_I*mean_p#print(np.allclose(var_I, cov_Ip))a = cov_Ip/(var_I+eps)b = mean_p-a*mean_Imean_a = cv2.blur(a,(win_size,win_size))mean_b = cv2.blur(b,(win_size,win_size))q = mean_a*I + mean_b#print(mean_II.dtype, cov_Ip.dtype, b.dtype, mean_a.dtype, I.dtype, q.dtype)return qdef multi_dim_guide_filter(I, p, r, eps):"""I 是三通道p 是单通道或者多通道图像"""out = np.zeros_like(p)if len(p.shape) == 2:out = multi_dim_guide_filter_single(I, p, r, eps)else:for c in range(p.shape[2]):out[..., c] = multi_dim_guide_filter_single(I, p[..., c], r, eps)return outdef multi_dim_guide_filter_single(I, p, r, eps):"""I : 导向图，多个通道  H, W, Cp : 单个通道        H, W, 1radius : 均值滤波核长度eps:"""# if len(p.shape) == 2:#     p = p[..., None]r = (r, r)mean_I_r = cv2.blur(I[..., 0], r);mean_I_g = cv2.blur(I[..., 1], r);mean_I_b = cv2.blur(I[..., 2], r);# variance of  I in each local patch: the matrix Sigma in Eqn(14).# Note the variance in each local patch is a  3x3  symmetric matrix:#           rr, rg, rb# Sigma =   rg, gg, gb#           rb, gb, bbvar_I_rr = cv2.blur(I[..., 0] * (I[..., 0]), r) - mean_I_r * (mean_I_r) + epsvar_I_rg = cv2.blur(I[..., 0] * (I[..., 1]), r) - mean_I_r * (mean_I_g)var_I_rb = cv2.blur(I[..., 0] * (I[..., 2]), r) - mean_I_r * (mean_I_b)var_I_gg = cv2.blur(I[..., 1] * (I[..., 1]), r) - mean_I_g * (mean_I_g) + epsvar_I_gb = cv2.blur(I[..., 1] * (I[..., 2]), r) - mean_I_g * (mean_I_b)var_I_bb = cv2.blur(I[..., 2] * (I[..., 2]), r) - mean_I_b * (mean_I_b) + eps# Inverse of Sigma + eps * Iinvrr = var_I_gg * (var_I_bb) - var_I_gb * (var_I_gb)invrg = var_I_gb * (var_I_rb) - var_I_rg * (var_I_bb)invrb = var_I_rg * (var_I_gb) - var_I_gg * (var_I_rb)invgg = var_I_rr * (var_I_bb) - var_I_rb * (var_I_rb)invgb = var_I_rb * (var_I_rg) - var_I_rr * (var_I_gb)invbb = var_I_rr * (var_I_gg) - var_I_rg * (var_I_rg)covDet = invrr * (var_I_rr) + invrg * (var_I_rg) + invrb * (var_I_rb)invrr /= covDetinvrg /= covDetinvrb /= covDetinvgg /= covDetinvgb /= covDetinvbb /= covDet# process pmean_p = cv2.blur(p, r)mean_Ip_r = cv2.blur(I[..., 0] * (p), r)mean_Ip_g = cv2.blur(I[..., 1] * (p), r)mean_Ip_b = cv2.blur(I[..., 2] * (p), r)# covariance  of(I, p) in each local patch.cov_Ip_r = mean_Ip_r - mean_I_r * (mean_p)cov_Ip_g = mean_Ip_g - mean_I_g * (mean_p)cov_Ip_b = mean_Ip_b - mean_I_b * (mean_p)a_r = invrr * (cov_Ip_r) + invrg * (cov_Ip_g) + invrb * (cov_Ip_b)a_g = invrg * (cov_Ip_r) + invgg * (cov_Ip_g) + invgb * (cov_Ip_b)a_b = invrb * (cov_Ip_r) + invgb * (cov_Ip_g) + invbb * (cov_Ip_b)b = mean_p - a_r * (mean_I_r) - a_g * (mean_I_g) - a_b * (mean_I_b)return (cv2.blur(a_r, r) * (I[..., 0])+ cv2.blur(a_g, r) * (I[..., 1])+ cv2.blur(a_b, r) * (I[..., 2])+ cv2.blur(b, r))

实验代码：

def compare_1_3channel(im, r, eps):"""分通道进行和一起进行，结果完全一致"""out1 = guided_filter(im, im, r, eps)out2 = np.zeros_like(out1)out2[..., 0] = guided_filter(im[..., 0], im[..., 0], r, eps)out2[..., 1] = guided_filter(im[..., 1], im[..., 1], r, eps)out2[..., 2] = guided_filter(im[..., 2], im[..., 2], r, eps)return out1, out2if __name__ == "__main__":file = r'D:\code\denoise\denoise_video\guide_filter_image\dd.png'kernel_size= 7r = kernel_size // 2eps = 0.002input = cv2.imread(file, 1)out1, out2 = compare_1_3channel(input, kernel_size, (eps * 255 * 255))cv2.imwrite(file[:-4] + 'out1.png', out1)    # 这个结果错误，因为uint8 * uint8仍然赋给了uint8# out2.png, out3.png, out4.png 结果基本一致input = input.astype(np.float32)  # 要转换为float类型out1, out2 = compare_1_3channel(input, kernel_size, (eps * 255 * 255))cv2.imwrite(file[:-4] + 'out2.png', out2)out1[..., 0] = cv2.ximgproc.guidedFilter(input[..., 0], input[..., 0], r,  (eps * 255 * 255))out1[..., 1] = cv2.ximgproc.guidedFilter(input[..., 1][..., None], input[..., 1][..., None], 3,  (eps * 255 * 255))out1[..., 2] = cv2.ximgproc.guidedFilter(input[..., 2][..., None], input[..., 2][..., None], 3,  (eps * 255 * 255))print('tt : ', out1.min(), out1.max())out4 = np.clip(out1 * 1, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + 'out4.png', out4)input = input / 255input = input.astype(np.float32)out1, out2 = compare_1_3channel(input, kernel_size, (eps)) # 注意0-1 和 0-255 在eps的差异。out3 = np.clip(out1 * 255, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + 'out3.png', out3)# out5.png 和 out6.png结果一致，利用灰度图作为导向图， 注意半径和kernel_size的区别。guide= cv2.cvtColor(input,cv2.COLOR_BGR2GRAY)out1 = cv2.ximgproc.guidedFilter(guide, input, r, (eps))out5 = np.clip(out1 * 255, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + 'out5.png', out5)out2[..., 0] = guided_filter(guide, input[..., 0], kernel_size, eps)out2[..., 1] = guided_filter(guide, input[..., 1], kernel_size, eps)out2[..., 2] = guided_filter(guide, input[..., 2], kernel_size, eps)out6 = np.clip(out2 * 255, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + 'out6.png', out6)plt.figure(figsize=(9, 14))plt.subplot(231), plt.axis('off'), plt.title("guidedFilter error")plt.imshow(cv2.cvtColor(out1, cv2.COLOR_BGR2RGB))plt.subplot(232), plt.axis('off'), plt.title("cv2.guidedFilter")plt.imshow(cv2.cvtColor(out2, cv2.COLOR_BGR2RGB))plt.subplot(233), plt.axis('off'), plt.title("cv2.guidedFilter")plt.imshow(cv2.cvtColor(out3, cv2.COLOR_BGR2RGB))plt.subplot(234), plt.axis('off'), plt.title("cv2.guidedFilter")plt.imshow(cv2.cvtColor(out4, cv2.COLOR_BGR2RGB))plt.subplot(235), plt.axis('off'), plt.title("cv2.guidedFilter")plt.imshow(cv2.cvtColor(out5, cv2.COLOR_BGR2RGB))plt.subplot(236), plt.axis('off'), plt.title("cv2.guidedFilter")plt.imshow(cv2.cvtColor(out6, cv2.COLOR_BGR2RGB))plt.tight_layout()plt.show()

输出结果

2.2 与 双边滤波比较

个人感觉引导滤波更好

完整代码如下：

if __name__=='__main__':file = r'D:\code\denoise\denoise_video\guide_filter_image\dd.png'kernel_size = 7r = kernel_size // 2eps1 = 0.004/2eps2 = 0.002/4input = cv2.imread(file, 1)input = input.astype(np.float32)  # 要转换为float类型out1 = guided_filter(input, input, kernel_size, eps1*255*255)out2 = cv2.bilateralFilter(input, kernel_size, eps2*255*255, eps2*255*255)out1 = np.clip(out1, 0, 255).astype(np.uint8)out2 = np.clip(out2, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + 'guide.png', out1)cv2.imwrite(file[:-4] + 'bi.png', out2)cv2.imshow("guide", out1)cv2.imshow("bi", out2)cv2.waitKey(0)

2.3 引导滤波应用，fathering

实验图像

实验code:

'''
导向滤波的应用: fathering
'''
def run_fathering():file_I = r'D:\code\denoise\denoise_video\guide_filter_image\apply\c.png'file_mask = r'D:\code\denoise\denoise_video\guide_filter_image\apply\d.png'I = cv2.imread(file_I, 1)I_gray = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)input = cv2.imread(file_mask, 0)kernel_size = 20r = kernel_size // 2eps1 = 0.000008 / 2I = I.astype(np.float32)I_gray = I_gray.astype(np.float32)input = input.astype(np.float32)  # 要转换为float类型out1 = cv2.ximgproc.guidedFilter(I, input, r, (eps1 * 255 * 255))out1 = np.clip(out1, 0, 255).astype(np.uint8)cv2.imwrite(file_mask[:-4] + 'guide.png', out1)out2 = cv2.ximgproc.guidedFilter(I_gray, input, r, (eps1 * 255 * 255))out2 = np.clip(out2, 0, 255).astype(np.uint8)cv2.imwrite(file_mask[:-4] + 'guide2.png', out2)out3 = guided_filter(I_gray, input, kernel_size, eps1 * 255 * 255)out3 = np.clip(out3, 0, 255).astype(np.uint8)cv2.imwrite(file_mask[:-4] + 'guide3.png', out3)print(I.shape, input.shape)out4 = multi_dim_guide_filter(I, input, kernel_size, eps1 * 255 * 255)out4 = np.clip(out4, 0, 255).astype(np.uint8)cv2.imwrite(file_mask[:-4] + 'guide4.png', out4)

out1 是彩色引导图，opencv库
out2 是灰度引导图，opencv库
out3 是灰度引导图，
out4 是彩色引导图

结果 out1和out4 接近一致，效果好。 out2和out3一致，效果存在问题

2.3 引导滤波应用，图像增强

图片

引导滤波结果稍好一些
实验code:

if __name__ == '__main__':file = r'D:\code\denoise\denoise_video\guide_filter_image\apply\e.png'I = cv2.imread(file, 1)I = I.astype(np.float32)p = Ikernel_size = 20r = kernel_size // 2eps1 = 0.008 / 2eps2 = 0.002 / 6out0 = cv2.bilateralFilter(p, kernel_size, eps2 * 255 * 255, eps2 * 255 * 255)  # 双边滤波out1 = multi_dim_guide_filter(I, p, kernel_size, (eps1 * 255 * 255)) # 多通道guideout2 = guided_filter(I, p, kernel_size, (eps1 * 255 * 255))          # 单通道各自guideout3 = cv2.ximgproc.guidedFilter(I, p, r, (eps1 * 255 * 255))        # 多通道guideout4 = (I - out0) * 2 + out0out5 = (I - out1) * 2 + out1out6 = (I - out2) * 2 + out2out7 = (I - out3) * 2 + out3out0 = np.clip(out0, 0, 255).astype(np.uint8)out1 = np.clip(out1, 0, 255).astype(np.uint8)out2 = np.clip(out2, 0, 255).astype(np.uint8)out3 = np.clip(out3, 0, 255).astype(np.uint8)  # out3 应该和 out1结果一致out4 = np.clip(out4, 0, 255).astype(np.uint8)out5 = np.clip(out5, 0, 255).astype(np.uint8)out6 = np.clip(out6, 0, 255).astype(np.uint8)  #out7 = np.clip(out7, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + '0.png', out0)cv2.imwrite(file[:-4] + '1.png', out1)cv2.imwrite(file[:-4] + '2.png', out2)cv2.imwrite(file[:-4] + '3.png', out3)cv2.imwrite(file[:-4] + '4.png', out4)cv2.imwrite(file[:-4] + '5.png', out5)cv2.imwrite(file[:-4] + '6.png', out6)cv2.imwrite(file[:-4] + '7.png', out7)

2.4 灰度图引导，和各自通道引导的效果差异

一致有个疑问，

1. 分别用r,g,b引导各自通道的效果
2. 利用灰度图引导各通道，比1滤波强度更大
3. 利用彩色图引导

哪种效果更好呢？ 实际使用的时候利用彩色图引导要相对复杂，计算量也更大。

def compare_1gray_3channel(im, r, eps):"""分通道进行和一起进行，结果完全一致"""out1 = guided_filter(im, im, r, eps)im_gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)out2 = np.zeros_like(out1)out2[..., 0] = guided_filter(im_gray, im[..., 0], r, eps)out2[..., 1] = guided_filter(im_gray, im[..., 1], r, eps)out2[..., 2] = guided_filter(im_gray, im[..., 2], r, eps)return out1, out2def run_compare_gray_guide():file = r'D:\code\denoise\denoise_video\guide_filter_image\compare\dd.png'kernel_size = 17r = kernel_size // 2eps1 = 0.02 / 2input = cv2.imread(file, 1)input = input.astype(np.float32)  # 要转换为float类型out1, out2 = compare_1gray_3channel(input, r, (eps1 * 255 * 255))out1 = np.clip(out1, 0, 255).astype(np.uint8)out2 = np.clip(out2, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + '1.png', out1)cv2.imwrite(file[:-4] + '2.png', out2)out3 = cv2.ximgproc.guidedFilter(I, p, r, (eps1 * 255 * 255))        # 多通道out3 = np.clip(out3, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + '3.png', out3)

2.5 不同参数设置影响

def parameter_tuning():file = r'D:\code\denoise\denoise_video\guide_filter_image\paramter_tuning\dd.png'kernel_size = 17r = kernel_size // 2eps1 = 0.02 / 2input = cv2.imread(file, 1)input = input.astype(np.float32)  # 要转换为float类型index = 0for r in np.arange(3, 21, 4):for eps in np.arange(0.000001, 0.00001, 0.000001):eps1 = eps * 255 * 255_, out2 = compare_1gray_3channel(input, r, eps1)out2 = np.clip(out2, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + '{}.png'.format(index), out2)index += 1for eps in np.arange(0.2, 1, 0.1):eps1 = eps * 255 * 255_, out2 = compare_1gray_3channel(input, r, eps1)out2 = np.clip(out2, 0, 255).astype(np.uint8)cv2.imwrite(file[:-4] + '{}.png'.format(index), out2)index += 1

3. 参考

[1]https://zhuanlan.zhihu.com/p/438206777 有详细解释 和 C++相关代码仓库
[2]https://blog.csdn.net/huixingshao/article/details/42834939 高级图像去雾算法的快速实现, guide filter用于去雾，解释的很清楚
[3]http://giantpandacv.com/academic/传统图像/一些有趣的图像算法/OpenCV图像处理专栏六 来自何凯明博士的暗通道去雾算法(CVPR 2009最佳论文)/去雾代码
[4]https://github.com/atilimcetin/guided-filter引导滤波C++code