模糊图像的阈值 - 第2部分 [英] Threshold of blurry image - part 2

问题描述

如何启动此模糊图像以使数字尽可能清晰？

在从形态上打开此帖子右上角的阈值图像。这不太好用。使用椭圆内核，只有3x3足够小，以避免完全删除图像，即使这样，数字也会出现明显的破坏：

编辑2：mmgp 可以更好地保留边缘，但计算速度较慢。

就阈值而言，您可以使用CV_OTSU标记。 cv :: threshold确定全局阈值的最佳值。局部阈值可能仍然更好，但应该使用双边或中值滤波器更好地工作

How can I threshold this blurry image to make the digits as clear as possible?

In a previous post, I tried adaptively thresholding a blurry image (left), which resulted in distorted and disconnected digits (right):

Since then, I've tried using a morphological closing operation as described in this post to make the brightness of the image uniform:

If I adaptively threshold this image, I don't get significantly better results. However, because the brightness is approximately uniform, I can now use an ordinary threshold:

This is a lot better than before, but I have two problems:

1. I had to manually choose the threshold value. Although the closing operation results in uniform brightness, the level of brightness might be different for other images.
2. Different parts of the image would do better with slight variations in the threshold level. For instance, the 9 and 7 in the top left come out partially faded and should have a lower threshold, while some of the 6s have fused into 8s and should have a higher threshold.

I thought that going back to an adaptive threshold, but with a very large block size (1/9th of the image) would solve both problems. Instead, I end up with a weird "halo effect" where the centre of the image is a lot brighter, but the edges are about the same as the normally-thresholded image:

Edit: remi suggested morphologically opening the thresholded image at the top right of this post. This doesn't work too well. Using elliptical kernels, only a 3x3 is small enough to avoid obliterating the image entirely, and even then there are significant breakages in the digits:

Edit2: mmgp suggested using a Wiener filter to remove blur. I adapted this code for Wiener filtering in OpenCV to OpenCV4Android, but it makes the image even blurrier! Here's the image before (left) and after filtering with my code and a 5x5 kernel:

Here is my adapted code, which filters in-place:

private void wiener(Mat input, int nRows, int nCols) { // I tried nRows=5 and nCols=5

    Mat localMean = new Mat(input.rows(), input.cols(), input.type());
    Mat temp = new Mat(input.rows(), input.cols(), input.type());
    Mat temp2 = new Mat(input.rows(), input.cols(), input.type());

    // Create the kernel for convolution: a constant matrix with nRows rows 
    // and nCols cols, normalized so that the sum of the pixels is 1.
    Mat kernel = new Mat(nRows, nCols, CvType.CV_32F, new Scalar(1.0 / (double) (nRows * nCols)));

    // Get the local mean of the input.  localMean = convolution(input, kernel)
    Imgproc.filter2D(input, localMean, -1, kernel, new Point(nCols/2, nRows/2), 0); 

    // Get the local variance of the input.  localVariance = convolution(input^2, kernel) - localMean^2 
    Core.multiply(input, input, temp);  // temp = input^2
    Imgproc.filter2D(temp, temp, -1, kernel, new Point(nCols/2, nRows/2), 0); // temp = convolution(input^2, kernel)
    Core.multiply(localMean, localMean, temp2); //temp2 = localMean^2
    Core.subtract(temp, temp2, temp); // temp = localVariance = convolution(input^2, kernel) - localMean^2  

    // Estimate the noise as mean(localVariance)
    Scalar noise = Core.mean(temp);

    // Compute the result.  result = localMean + max(0, localVariance - noise) / max(localVariance, noise) * (input - localMean)

    Core.max(temp, noise, temp2); // temp2 = max(localVariance, noise)

    Core.subtract(temp, noise, temp); // temp = localVariance - noise
    Core.max(temp, new Scalar(0), temp); // temp = max(0, localVariance - noise)

    Core.divide(temp, temp2, temp);  // temp = max(0, localVar-noise) / max(localVariance, noise)

    Core.subtract(input, localMean, input);  // input = input - localMean
    Core.multiply(temp, input, input); // input = max(0, localVariance - noise) / max(localVariance, noise) * (input - localMean)
    Core.add(input, localMean, input); // input = localMean + max(0, localVariance - noise) / max(localVariance, noise) * (input - localMean)
}

解决方案

Some hints that you might try out:

• Apply the morphological opening in your original thresholded image (the one which is noisy at the right of the first picture). You should get rid of most of the background noise and be able to reconnect the digits.

• Use a different preprocessing of your original image instead of morpho closing, such as median filter (tends to blur the edges) or bilateral filtering which will preserve better the edges but is slower to compute.

• As far as threshold is concerned, you can use CV_OTSU flag in the cv::threshold to determine an optimal value for a global threshold. Local thresholding might still be better, but should work better with the bilateral or median filter

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