SAR+BM3D 算法
时间: 2023-12-10 15:35:14 浏览: 31
SAR(Synthetic Aperture Radar)图像去噪是一个重要的问题,BM3D算法是一种常用的SAR图像去噪算法。下面是SAR+BM3D算法的步骤:
1. 对SAR图像进行小波变换,得到各个尺度的小波系数。
```python
import pywt
coeffs = pywt.wavedec2(sar_image, 'db4', level=3)
```
2. 对每个小波系数进行分组,每组大小为$N_1 \times N_1$,并计算每组的3D块。
```python
import numpy as np
N1 = 8
N2 = 8
N3 = 8
blocks = []
for coeff in coeffs:
for i in range(0, coeff.shape[0]-N1+1, N1):
for j in range(0, coeff.shape[1]-N2+1, N2):
for k in range(0, coeff.shape[2]-N3+1, N3):
block = coeff[i:i+N1, j:j+N2, k:k+N3]
blocks.append(block)
blocks = np.array(blocks)
```
3. 对每个3D块进行相似性匹配,得到每个块的相似块集合。
```python
from sklearn.neighbors import NearestNeighbors
k = 16
neigh = NearestNeighbors(n_neighbors=k)
neigh.fit(blocks.reshape(blocks.shape[0], -1))
similarity_matrix = neigh.kneighbors_graph(mode='distance')
similarity_matrix = similarity_matrix.toarray()
```
4. 对每个块的相似块集合进行3D变换,得到每个块的3D变换系数。
```python
from sklearn.decomposition import PCA
pca = PCA(n_components=3)
transformed_blocks = []
for i in range(blocks.shape[0]):
similar_blocks = blocks[similarity_matrix[i] > 0]
transformed_block = pca.fit_transform(similar_blocks.reshape(similar_blocks.shape[0], -1))
transformed_blocks.append(transformed_block)
transformed_blocks = np.array(transformed_blocks)
```
5. 对每个块的3D变换系数进行硬阈值处理,得到每个块的去噪系数。
```python
sigma = 0.1
threshold = sigma * np.sqrt(2 * np.log(blocks.shape[0] * transformed_blocks.shape[1]))
denoised_transformed_blocks = np.where(np.abs(transformed_blocks) < threshold, 0, transformed_blocks)
```
6. 对每个块的去噪系数进行3D逆变换,得到每个块的去噪结果。
```python
denoised_blocks = []
for i in range(blocks.shape[0]):
denoised_transformed_block = denoised_transformed_blocks[i]
denoised_block = pca.inverse_transform(denoised_transformed_block)
denoised_blocks.append(denoised_block)
denoised_blocks = np.array(denoised_blocks)
```
7. 对每个块的去噪结果进行加权平均,得到SAR图像的去噪结果。
```python
weights = np.ones((N1, N2, N3))
denoised_image = np.zeros_like(sar_image)
count = np.zeros_like(sar_image)
index = 0
for coeff in coeffs:
for i in range(0, coeff.shape[0]-N1+1, N1):
for j in range(0, coeff.shape[1]-N2+1, N2):
for k in range(0, coeff.shape[2]-N3+1, N3):
denoised_block = denoised_blocks[index]
denoised_image[i:i+N1, j:j+N2, k:k+N3] += denoised_block * weights
count[i:i+N1, j:j+N2, k:k+N3] += weights
index += 1
denoised_image /= count
```