Python bayer插值

Bayer插值是一种用于从Bayer格式的图像数据中重建全彩色图像的技术。Bayer格式是一种常见的图像传感器排列方式，其中每个像素只记录一种颜色（红色、绿色或蓝色），而不是所有三种颜色。Bayer插值算法通过估算缺失的颜色值来生成每个像素的完整RGB值。

Bayer插值的常见方法包括：

1. **最近邻插值**：最简单的插值方法，直接使用相邻像素的颜色值来填充缺失的颜色值。
2. **双线性插值**：通过计算相邻像素的颜色值的平均值来填充缺失的颜色值。
3. **双三次插值**：使用更复杂的数学模型来估算缺失的颜色值，通常会生成更高质量的图像。

以下是一个简单的双线性插值示例代码：

import numpy as np
from PIL import Image

def bayer_interpolation(bayer_image):
    height, width = bayer_image.shape
    rgb_image = np.zeros((height, width, 3), dtype=np.uint8)

    for i in range(height):
        for j in range(width):
            if (i % 2 == 0) and (j % 2 == 0):
                # Green pixel
                rgb_image[i, j, 1] = bayer_image[i, j]
                rgb_image[i, j, 0] = (bayer_image[i, j-1] + bayer_image[i, j+1]) // 2
                rgb_image[i, j, 2] = (bayer_image[i-1, j] + bayer_image[i+1, j]) // 2
            elif (i % 2 == 0) and (j % 2 != 0):
                # Red pixel
                rgb_image[i, j, 0] = bayer_image[i, j]
                rgb_image[i, j, 1] = (bayer_image[i, j-1] + bayer_image[i, j+1] + bayer_image[i-1, j] + bayer_image[i+1, j]) // 4
                rgb_image[i, j, 2] = (bayer_image[i-1, j-1] + bayer_image[i-1, j+1] + bayer_image[i+1, j-1] + bayer_image[i+1, j+1]) // 4
            elif (i % 2 != 0) and (j % 2 == 0):
                # Blue pixel
                rgb_image[i, j, 2] = bayer_image[i, j]
                rgb_image[i, j, 1] = (bayer_image[i, j-1] + bayer_image[i, j+1] + bayer_image[i-1, j] + bayer_image[i+1, j]) // 4
                rgb_image[i, j, 0] = (bayer_image[i-1, j-1] + bayer_image[i-1, j+1] + bayer_image[i+1, j-1] + bayer_image[i+1, j+1]) // 4
            else:
                # Green pixel
                rgb_image[i, j, 1] = bayer_image[i, j]
                rgb_image[i, j, 0] = (bayer_image[i, j-1] + bayer_image[i, j+1]) // 2
                rgb_image[i, j, 2] = (bayer_image[i-1, j] + bayer_image[i+1, j]) // 2

    return rgb_image

# 示例使用
bayer_image = np.array([
    [1, 2, 1, 2],
    [2, 3, 2, 3],
    [1, 2, 1, 2],
    [2, 3, 2, 3]
], dtype=np.uint8)

rgb_image = bayer_interpolation(bayer_image)
image = Image.fromarray(rgb_image)
image.show()