在MATLAB中进行去模糊运动模糊图像 [英] Deblurring motion blurred images in MATLAB

问题描述

我目前正在制作节目，我需要在照片上自动执行运动不模糊。目前，我为 LEN 和 THETA 为循环设置 ，猜测来自 LEN 0:50 和 THETA 来自 1：180 。以这种方式产生大量运动不模糊的图像 - 一些是正确的，一些是错误的。现在这是我的问题：我如何确定哪一组参数产生最接近原始照片的参数？

我正在考虑使用像素比较。对此有何看法？

这是我生成的图片示例：

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因此，要在代码中使用它，循环的应该如下所示：

  psnr_max = -realmax; 
 for LEN = 0：50 
 for THETA = 1：180 
％// Unblur image 
％// ... 
％// ... 
％//计算PSNR 
 mse = mean（mean（（im2double（I） -  im2double（K））。^ 2,1），2）; 
 psnr = 10 * log10（1 ./ mean（mse，3））; 
 if（psnr> psnr_max）％//获得最大的PSNR并获得
 LEN_final = LEN; ％//参数使得
 THETA_final = THETA; 
 psnr_max = psnr; 
结束
结束
结束
  





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这个循环将遍历每对 LEN 和 THETA ，以及 LEN_final ， THETA_final 将是那些为您提供最佳重建（非模糊）图像的参数。

I'm currently working on program and I need to automatic perform motion un-blurring on a picture. Currently, I make a for loop for LEN and THETA, guessing from LEN 0:50 and THETA from 1:180. There are plenty of motion un-blurred pictures produce in this way - some correct and some are wrong. Now here is my problem: How do I actually determine which set of parameters yields the one most closest to original photo?

I'm thinking of using pixel comparison. Any idea on this?

Here's a pictorial example of what I generated:

http://dl.dropboxusercontent.com/u/81112742/Capture.JPG

解决方案

If you have access to the original clean image, I would compute the Peak Signal to Noise Ratio (PSNR) for all of the images you have generated, then choose the one with the highest PSNR. Amro posted a very nice post on how to compute this for images and can be found here: https://stackoverflow.com/a/16265510/3250829

However, for self-containment, I'll post the code to do it here. Supposing that your original image is stored in the variable I, and supposing your reconstructed (un-blurred) image is stored in the variable K. Therefore, to calculate the PSNR, you would need to calculate the Mean Squared Error first, then use it to calculate the PSNR. In other words:

mse = mean(mean((im2double(I) - im2double(K)).^2, 1), 2);
psnr = 10 * log10(1 ./ mean(mse,3));

The equations for MSE and PSNR are:

^{Source: Wikipedia}

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As such, to use this in your code, your for loops should look something like this:

psnr_max = -realmax;
for LEN = 0 : 50
    for THETA = 1 : 180
        %// Unblur the image
        %//...
        %//...
        %// Compute PSNR
        mse = mean(mean((im2double(I) - im2double(K)).^2, 1), 2);
        psnr = 10 * log10(1 ./ mean(mse,3));
        if (psnr > psnr_max) %// Get largest PSNR and get the
            LEN_final = LEN; %// parameters that made this so
            THETA_final = THETA;
            psnr_max = psnr;
        end
    end
end

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This loop will go through each pair of LEN and THETA, and LEN_final, THETA_final will be those parameters that gave you the best reconstruction (un-blurring) of the image.

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