在图像处理领域中,去马赛克(Demosaicing)是一项关键技术,用于从单色彩滤波阵列(CFA)图像恢复全彩图像。本文将介绍一种简单的线性插值去马赛克算法,并将其从MATLAB代码转换为Python代码。最终结果将展示如何从Bayer格式的图像数据恢复出RGB全彩图像。
马赛克图像是一种通过在传感器上覆盖彩色滤光片阵列(CFA)生成的单通道图像。最常见的CFA模式是Bayer模式,其中包括红(R)、绿(G)和蓝(B)三种滤光片,以特定模式排列。去马赛克过程就是从这种单通道图像中恢复出三通道(RGB)的彩色图像。
本文实现的去马赛克算法是基于简单线性插值的。它利用邻近像素的值来估计每个像素点的RGB值。具体步骤如下:
import numpy as np
import matplotlib.pyplot as plt
def read_raw(file_path, bits, width, height):
with open(file_path, 'rb') as f:
raw_data = np.fromfile(f, dtype=np.uint8)
bayer_data = raw_data.reshape((height, width))
return bayer_data
def demosaic(bayer_data, width, height):
# 扩展图像以便于计算边缘像素
bayer_padding = np.zeros((height + 2, width + 2), dtype=np.float32)
bayer_padding[1:height+1, 1:width+1] = bayer_data
bayer_padding[0, :] = bayer_padding[2, :]
bayer_padding[height+1, :] = bayer_padding[height, :]
bayer_padding[:, 0] = bayer_padding[:, 2]
bayer_padding[:, width+1] = bayer_padding[:, width]
# 插值的主要代码
im_dst = np.zeros((height + 2, width + 2, 3), dtype=np.float32)
for ver in range(1, height + 1):
for hor in range(1, width + 1):
if (ver % 2 == 1 and hor % 2 == 1): # Red pixel
im_dst[ver, hor, 0] = bayer_padding[ver, hor]
im_dst[ver, hor, 1] = (bayer_padding[ver-1, hor] + bayer_padding[ver+1, hor] +
bayer_padding[ver, hor-1] + bayer_padding[ver, hor+1]) / 4
im_dst[ver, hor, 2] = (bayer_padding[ver-1, hor-1] + bayer_padding[ver-1, hor+1] +
bayer_padding[ver+1, hor-1] + bayer_padding[ver+1, hor+1]) / 4
elif (ver % 2 == 0 and hor % 2 == 0): # Blue pixel
im_dst[ver, hor, 2] = bayer_padding[ver, hor]
im_dst[ver, hor, 1] = (bayer_padding[ver-1, hor] + bayer_padding[ver+1, hor] +
bayer_padding[ver, hor-1] + bayer_padding[ver, hor+1]) / 4
im_dst[ver, hor, 0] = (bayer_padding[ver-1, hor-1] + bayer_padding[ver-1, hor+1] +
bayer_padding[ver+1, hor-1] + bayer_padding[ver+1, hor+1]) / 4
elif (ver % 2 == 1 and hor % 2 == 0): # Green pixel (on Red row)
im_dst[ver, hor, 1] = bayer_padding[ver, hor]
im_dst[ver, hor, 0] = (bayer_padding[ver, hor-1] + bayer_padding[ver, hor+1]) / 2
im_dst[ver, hor, 2] = (bayer_padding[ver-1, hor] + bayer_padding[ver+1, hor]) / 2
elif (ver % 2 == 0 and hor % 2 == 1): # Green pixel (on Blue row)
im_dst[ver, hor, 1] = bayer_padding[ver, hor]
im_dst[ver, hor, 2] = (bayer_padding[ver, hor-1] + bayer_padding[ver, hor+1]) / 2
im_dst[ver, hor, 0] = (bayer_padding[ver-1, hor] + bayer_padding[ver+1, hor]) / 2
im_dst = im_dst[1:height+1, 1:width+1, :]
return im_dst
# ------------原始格式----------------
file_path = '../images/kodim19_8bits_RGGB.raw'
bayer_format = 'RGGB'
width = 512
height = 768
bits = 8
# --------------------------------------
bayer_data = read_raw(file_path, bits, width, height)
plt.figure()
plt.imshow(bayer_data, cmap='gray')
plt.title('raw image')
plt.show()
im_dst = demosaic(bayer_data, width, height).astype(np.uint8)
plt.figure()
plt.imshow(im_dst)
plt.title('demosaic image')
plt.show()
org_image = plt.imread('../images/kodim19.png')
plt.figure()
plt.imshow(org_image)
plt.title('org image')
plt.show()