反向(去除)抗混叠滤波器 [英] Reverse (remove) anti-aliasing filter
问题描述
我有一组抗锯齿的灰度PNG图像.我需要知道如何以编程方式还原抗锯齿效果并再次获得清晰的边缘.
I have a set of anti-aliased greyscale PNG images. I need to know how to programatically revert the anti-aliasing effect and get sharp edges again.
我正在使用GDI +，但对代码的兴趣不大.如果可以建立这样的矩阵，我需要一个算法，也许是一个卷积滤波器.
I'm using GDI+ but I am less interested in code. I need an algorithm, maybe a convolution filter, if such a matrix can be built.
(应该)灰度图像仅包含6种颜色(或不同的灰色阴影).这样一来，以后我就可以使用颜色查找"滤镜为它们重新着色.但是，在保存图像时，Photoshop会自动应用抗锯齿功能，以使边缘模糊(因为启用了Bicubic Interpolation模式).我需要恢复这种效果.
The greyscale images (should) contain only 6 colors (or different shades of grey). This is so that later on I can re-color them using a Color-Lookup filter. However, when the images where saved, Photoshop automatically applied anti-aliasing so the edges were blurred (because the Bicubic Interpolation mode was enabled). I need to revert that effect.
这里是一个例子:
这是Photoshop的屏幕截图
This is a screenshot from Photoshop
有人建议我应用锐化"滤镜，因此我在photoshop上尝试了该滤镜.这是它的外观:
Someone suggested that I should apply a Sharpen filter, so I tried it on photoshop. Here is how it looks:
即使外部边缘很好，但两种不同颜色相遇的边缘仍显示出伪像.
Even though the outer edges are fine, the edges where 2 different colors meet show artifacts.
这就是我最终这样做的方式.它是非常即兴的，可能可以做得更好，更快，但是我找不到更好的解决方案.
This is how I ended up doing it. It is very much improvised and can probably be done better and faster, but I couldn't find any better solution.
想法是遍历每个像素，获取其直接邻居，并将其颜色与其像素进行比较.如果至少有2个相同颜色的像素作为后盾，它将检查是否也支持相邻像素.如果没有，它将用自己的像素替换相邻像素.
The idea is to iterate over each pixel, get its direct neighbors and compare its color to theirs. If it's backed by at least 2 pixels of same color, it checks if the neighbor pixel is backed as well. If not, it replaces the neighbor pixel with its own.
代码:
private static void Resample(Bitmap bmp)
{
// First we look for the most prominent colors
// i.e. They make up at least 1% of the image
Hashtable stats = new Hashtable();
for (int x = 0; x < bmp.Width; x++)
{
for (int y = 0; y < bmp.Height; y++)
{
Color px = bmp.GetPixel(x, y);
if (px.A == 0)
continue;
Color pxS = Color.FromArgb(255, px);
if (stats.ContainsKey(pxS.ToArgb()))
stats[pxS.ToArgb()] = (int)stats[pxS.ToArgb()] + 1;
else
stats.Add(pxS.ToArgb(), 1);
}
}
float totalSize = bmp.Width*bmp.Height;
float minAccepted = 0.01f;
List<int> selectedColors = new List<int>();
// Make up a list with the selected colors
foreach (int key in stats.Keys)
{
int total = (int)stats[key];
if (((float)total / totalSize) > minAccepted)
selectedColors.Add(key);
}
// Keep growing the zones with the selected colors to cover the invalid colors created by the anti-aliasing
while (GrowSelected(bmp, selectedColors));
}
private static bool GrowSelected(Bitmap bmp, List<int> selectedColors)
{
bool flag = false;
for (int x = 0; x < bmp.Width; x++)
{
for (int y = 0; y < bmp.Height; y++)
{
Color px = bmp.GetPixel(x, y);
if (px.A == 0)
continue;
Color pxS = Color.FromArgb(255, px);
if (selectedColors.Contains(pxS.ToArgb()))
{
if (!isBackedByNeighbors(bmp, x, y))
continue;
List<Point> neighbors = GetNeighbors(bmp, x, y);
foreach(Point p in neighbors)
{
Color n = bmp.GetPixel(p.X, p.Y);
if (!isBackedByNeighbors(bmp, p.X, p.Y))
bmp.SetPixel(p.X, p.Y, Color.FromArgb(n.A, pxS));
}
}
else
{
flag = true;
}
}
}
return flag;
}
private static List<Point> GetNeighbors(Bitmap bmp, int x, int y)
{
List<Point> neighbors = new List<Point>();
for (int i = x - 1; i > 0 && i <= x + 1 && i < bmp.Width; i++)
for (int j = y - 1; j > 0 && j <= y + 1 && j < bmp.Height; j++)
neighbors.Add(new Point(i, j));
return neighbors;
}
private static bool isBackedByNeighbors(Bitmap bmp, int x, int y)
{
List<Point> neighbors = GetNeighbors(bmp, x, y);
Color px = bmp.GetPixel(x, y);
int similar = 0;
foreach (Point p in neighbors)
{
Color n = bmp.GetPixel(p.X, p.Y);
if (Color.FromArgb(255, px).ToArgb() == Color.FromArgb(255, n).ToArgb())
similar++;
}
return (similar > 2);
}
结果: 原始图片: http://i.imgur.com/8foQwFe.png
Result: Original Image: http://i.imgur.com/8foQwFe.png
消除锯齿的结果: http://i.imgur.com/w6gELWJ.png
推荐答案
过滤器的反向过程称为反卷积(这是一般逆问题的一种特殊情况).
反卷积有两种类型:
The reversing procedure of a filter is called Deconvolution (Which is a specific case of the General Inverse Problem).
There are two types of Deconvolution:
- 非盲反卷积-已知图像操作(例如，已知所应用的低通滤波器的系数).
- 盲反卷积-在具体应用的滤波器未知的情况下，仅假设有关它的一些假设(例如LPF或空间不变等).
这些算法(通常都是复杂算法)需要时间(除非使用朴素的维纳过滤器"方法).
Those are usually (Any of them) complex algorithms which take time (Unless using the naive "Wiener Filter" approach).
假设滤波器是某种LPF穷人的解决方案将是某种高通滤波器(HPF). 这些中的任何一个都会给出更清晰的图像"和增强的边缘"的外观. 这种类型的已知过滤器是锐化蒙版":
Assuming the filter is some kind of LPF poor's man solution would be some kind High Pass Filter (HPF). Any of those would give a look of "Sharper Image" and "Enhanced Edges". Known filter of this type is the Unsharp Mask:
- 在图像上应用LPF(通常使用具有给定STD的高斯模糊).我们称之为lpfImage.
- 计算差异图像:diffImage = originalImage-lpfImage.
- 锐化蒙版图像"的给出方式为:usmImage = originalImage +(alpha * diffImage)
其中alpha是锐化"级别的预定义缩放比例.
- Apply LPF on the image (Usually using Gaussian Blur with a given STD). Let's call it lpfImage.
- Calculate the difference image: diffImage = originalImage - lpfImage.
- The "Unsharp Mask Image" is given by: usmImage = originalImage + (alpha * diffImage)
Wherealphais a predefined scaling factor of the "Sharpening" level.
享受...
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