ISP模块之RAW DATA去噪（一）

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ISP模块之RAW DATA去噪（一）
ISP(Image Signal Processor),图像信号处理器，主要用来对前端图像传感器输出信号处理的单元，主要用于手机，监控摄像头等设备上。

RAW DATA，可以理解为：RAW图像就是CMOS或者CCD图像感应器将捕捉到的光源信号转化为数字信号的原始数据，是无损的，包含了物体原始的颜色信息等。RAW数据格式一般采用的是Bayer排列方式，通过滤波光片，产生彩色滤波阵列(CFA)，鉴于人眼对绿色波段的色彩比较敏感，Bayer数据格式中包含了50%的绿色信息，以及各25%的红色和蓝色信息。

Bayer排列格式有以下4种：

1.| R | G | 2.| B | G | 3.| G | R | 4.| G | B |

| G | B | | G | R | | B | G | | R | G |

在ISP处理模块的第一部分，就是需要对CFA DATA进行去噪操作。普通的去噪方式针对Bayer数据格式是不合适的，需要进行变换后才能进行处理。

一、中值滤波CFA(Color Filter Array)Data去噪方法

首先，让我们一起来回顾一下中值滤波的算法原理以及优缺点，然后给出示意的算法效果图。

中值滤波，顾名思义就是将滤波器里面所有像素值进行排序，然后用中间值替代当前像素点值。常用的中值滤波器有3X3,5X5等。

中值滤波的有点在于，实现简单，能够有效的消除椒盐噪声以及其他脉冲型噪声。缺点也是所有去噪算法所共有的，就是平滑模糊了图像的内容，有些角点以及边缘的信息损失。

对CFA DATA进行去噪时，需要将不同的颜色通道分开进行处理，这样是为了防止在平滑过程中将有用的颜色信息丢掉，比如说，由绿色信息包围的蓝色像素值与其相差很大时，此时就会被认为是噪声被处理掉，然而真实情况是，该区域的蓝色信息都是很大的。所以各通道单独处理的话是有利于保护颜色信息的。在我的处理过程中，是将原CFA DATA分成4块-R,G1,G2,B,分块去噪完成后再重新恢复到原来的位置，这样整个过程就完成了。

下面给出参考的中值滤波和主程序的C++（MFC）代码：

主函数：
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void main()

{



    /*******开始编写中值滤波去噪模块--2015.07.27***********/

    //针对R分量块进行去噪

    pNewDoc->m_RBlock  = new unsigned short [m_Height*m_Width/4];

    pNewDoc->m_G1Block = new unsigned short [m_Height*m_Width/4];

    pNewDoc->m_G2Block = new unsigned short [m_Height*m_Width/4];

    pNewDoc->m_BBlock  = new unsigned short [m_Height*m_Width/4];



    unsigned short* smoothR  = new unsigned short[m_Height*m_Width/4];

    unsigned short* smoothG1 = new unsigned short[m_Height*m_Width/4];

    unsigned short* smoothG2 = new unsigned short[m_Height*m_Width/4];

    unsigned short* smoothB  = new unsigned short[m_Height*m_Width/4];

    for (int i = 0; i < m_Height/2 ;i ++ )

    {

        for(int j = 0; j < m_Width/2 ; j ++ )

        {

            pNewDoc->m_RBlock [i*m_Width/2 + j] = m_RawImage[i*m_Width*2 + j*2];

            pNewDoc->m_G1Block[i*m_Width/2 + j] = m_RawImage[i*m_Width*2 + j*2 + 1];

            pNewDoc->m_G2Block[i*m_Width/2 + j] = m_RawImage[i*m_Width*2 + m_Width + j*2];

            pNewDoc->m_BBlock [i*m_Width/2 + j] = m_RawImage[i*m_Width*2 + m_Width + j*2 + 1];

        }

    }

    medianFilter(pNewDoc->m_RBlock,smoothR,m_Width/2,m_Height/2);   //针对R分量块进行去噪

    medianFilter(pNewDoc->m_G1Block,smoothG1,m_Width/2,m_Height/2); //针对G1分量块进行去噪

    medianFilter(pNewDoc->m_G2Block,smoothG2,m_Width/2,m_Height/2); //针对G2分量块进行去噪

    medianFilter(pNewDoc->m_BBlock,smoothB,m_Width/2,m_Height/2);   //针对B分量块进行去噪



    //反过来构造去噪去噪后的raw data

    for (int i = 0; i < m_Height/2 - 1;i ++ )

    {

        for(int j = 0; j < m_Width/2-1; j ++ )

        {

            pNewDoc->m_ImageNR[i*m_Width*2 + j*2] = smoothR[i*m_Width/2 + j];

            pNewDoc->m_ImageNR[i*m_Width*2 + j*2 + 1] = smoothG1[i*m_Width/2 + j];

            pNewDoc->m_ImageNR[i*m_Width*2 + m_Width + j*2] = smoothG2[i*m_Width/2 + j];

            pNewDoc->m_ImageNR[i*m_Width*2 + m_Width + j*2 + 1] = smoothB[i*m_Width/2 + j];



        }

    }

    /***********中值滤波模块完成--2015.07.27********************/

    //SaveImageData(pNewDoc->m_ImageNR, m_Height ,m_Width,"E:\m_ImageNR.bmp");

    SetDisplayRawImage( pNewDoc->m_ImageNR, m_Height ,m_Width, m_RawBitType,pNewDoc->m_Image);

}
```
void main()
{

	/*******开始编写中值滤波去噪模块--2015.07.27***********/
	//针对R分量块进行去噪
	pNewDoc->m_RBlock  = new unsigned short [m_Height*m_Width/4];
	pNewDoc->m_G1Block = new unsigned short [m_Height*m_Width/4];
	pNewDoc->m_G2Block = new unsigned short [m_Height*m_Width/4];
	pNewDoc->m_BBlock  = new unsigned short [m_Height*m_Width/4];

	unsigned short* smoothR  = new unsigned short[m_Height*m_Width/4];
	unsigned short* smoothG1 = new unsigned short[m_Height*m_Width/4];
	unsigned short* smoothG2 = new unsigned short[m_Height*m_Width/4];
	unsigned short* smoothB  = new unsigned short[m_Height*m_Width/4];
	for (int i = 0; i < m_Height/2 ;i ++ )
	{
		for(int j = 0; j < m_Width/2 ; j ++ )
		{
			pNewDoc->m_RBlock [i*m_Width/2 + j] = m_RawImage[i*m_Width*2 + j*2];
			pNewDoc->m_G1Block[i*m_Width/2 + j] = m_RawImage[i*m_Width*2 + j*2 + 1];
			pNewDoc->m_G2Block[i*m_Width/2 + j] = m_RawImage[i*m_Width*2 + m_Width + j*2];
			pNewDoc->m_BBlock [i*m_Width/2 + j] = m_RawImage[i*m_Width*2 + m_Width + j*2 + 1];
		}
	}
	medianFilter(pNewDoc->m_RBlock,smoothR,m_Width/2,m_Height/2);   //针对R分量块进行去噪
	medianFilter(pNewDoc->m_G1Block,smoothG1,m_Width/2,m_Height/2); //针对G1分量块进行去噪
	medianFilter(pNewDoc->m_G2Block,smoothG2,m_Width/2,m_Height/2); //针对G2分量块进行去噪
	medianFilter(pNewDoc->m_BBlock,smoothB,m_Width/2,m_Height/2);   //针对B分量块进行去噪

	//反过来构造去噪去噪后的raw data
	for (int i = 0; i < m_Height/2 - 1;i ++ )
	{
		for(int j = 0; j < m_Width/2-1; j ++ )
		{
			pNewDoc->m_ImageNR[i*m_Width*2 + j*2] = smoothR[i*m_Width/2 + j];
			pNewDoc->m_ImageNR[i*m_Width*2 + j*2 + 1] = smoothG1[i*m_Width/2 + j]; 
			pNewDoc->m_ImageNR[i*m_Width*2 + m_Width + j*2] = smoothG2[i*m_Width/2 + j];
			pNewDoc->m_ImageNR[i*m_Width*2 + m_Width + j*2 + 1] = smoothB[i*m_Width/2 + j];

		}
	}
	/***********中值滤波模块完成--2015.07.27********************/
	//SaveImageData(pNewDoc->m_ImageNR, m_Height ,m_Width,"E:\m_ImageNR.bmp");
	SetDisplayRawImage( pNewDoc->m_ImageNR, m_Height ,m_Width, m_RawBitType,pNewDoc->m_Image);
}
```
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<pre name="code" class="html">void medianFilter (unsigned short* corrupted, unsigned short* smooth, int width, int height)

{



    memcpy ( smooth, corrupted, width*height*sizeof(unsigned short) );

    for (int j=1;j<height-1;j++)

    {

        for (int i=1;i<width-1;i++)

        {

            int k = 0;

            unsigned short window[9];

            for (int jj = j - 1; jj < j + 2; ++jj)

                for (int ii = i - 1; ii < i + 2; ++ii)

                    window[k++] = corrupted[jj * width + ii];

            //   Order elements (only half of them)

            for (int m = 0; m < 5; ++m)

            {

                int min = m;

                for (int n = m + 1; n < 9; ++n)

                    if (window[n] < window[min])

                        min = n;

                //   Put found minimum element in its place

                unsigned short temp = window[m];

                window[m] = window[min];

                window[min] = temp;

            }

            smooth[ j*width+i ] = window[4];

        }

    }

} <span style="font-family: Arial, Helvetica, sans-serif;"> </span>
```
<pre name="code" class="html">void medianFilter (unsigned short* corrupted, unsigned short* smooth, int width, int height)  
{  
      
    memcpy ( smooth, corrupted, width*height*sizeof(unsigned short) );  
    for (int j=1;j<height-1;j++)  
    {  
        for (int i=1;i<width-1;i++)  
        {  
            int k = 0;  
            unsigned short window[9];  
            for (int jj = j - 1; jj < j + 2; ++jj)  
                for (int ii = i - 1; ii < i + 2; ++ii)  
                    window[k++] = corrupted[jj * width + ii];  
            //   Order elements (only half of them)  
            for (int m = 0; m < 5; ++m)  
            {  
                int min = m;  
                for (int n = m + 1; n < 9; ++n)  
                    if (window[n] < window[min])  
                        min = n;  
                //   Put found minimum element in its place  
                unsigned short temp = window[m];  
                window[m] = window[min];  
                window[min] = temp;  
            }
			smooth[ j*width+i ] = window[4];  
        }  
    }  
} <span style="font-family: Arial, Helvetica, sans-serif;"> </span>
```
[html] view plain copy print ?
中值滤波函数是在网上找的代码，由于比较基础，就直接拿过来用了，侵删

去噪前后效果图：

下一篇文章，我将主要给大家展示一下BM3D算法RAW DATA去噪效果，谢谢。
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原文地址：https://www.cnblogs.com/whw19818/p/5765967.html