用于OpenCV的抖动校正的图像校正 [英] Image Rectification for Shake Correction on OpenCV

问题描述

我有2张未经校准的相机拍摄的同一场景的照片.图片的角度和比例(缩放)略有不同，我想将它们叠加起来，拒绝任何形式的抖动.换句话说，我应该对其进行转换，以使抖动变得难以察觉，请执行运动补偿.

I've 2 pictures of the same scene from an uncalibrated camera. The pics are from a slightly different angle and scale(zoom) and I'd like to superpose them, rejecting any kind of shake. In other words, I should transform them so the shake becomes imperceptible, do a Motion Compensation.

我已经尝试过使用简单的SURF(特征)检测器和同形照相术，但有时结果并不令人满意.因此，我正在考虑尝试图像校正来补偿运动. -进行轻微更改(例如用户晃动)是否可以使用? -拒绝这两个帧的抖动真的有效吗?而对于更大的图片缓冲区(可能是10张)? -有人知道它是否可以解决缩放差异(图像中的不同缩放比例)吗? -该算法真正的作用是什么?会将两张图片都转换为第三方向吗?

I've already tried using a simple SURF (feature) detector along with Homography but sometimes the result isn't satisfactory. So I am thinking about trying Image Rectification to compensate the motion. - Would it work with slight changes, such as user shake? - Would it really work to reject shake for these 2 frames? And for a bigger buffer of pictures (10 maybe)? - Anyone knows if it would fix scale disparity (different zoom in the images)? - What the algorithm really do? Will it transform both pictures into a third orientation?

如果有更好的解决方案，我将很高兴知道=)

If there is a better solution, I would be glad to know =)

编辑

我的目的不是补偿模糊运动，而是补偿位移本身.例如，在此文件作者通过图像校正来补偿两个摄像机之间的角度差.它实际上是如何工作的?它是否总是创建中间的图片方向，或者我可以指定其中一张图片保持静止?

I don't aim to compensate blur motion but the displacement itself. For example, in this file the author compensates the angle difference between two cameras by Image Rectification. How does it actually work? Does it always create an intermediate picture orientation or can I specify that one of the pictures shall remains still??

我还能将其应用于许多框架吗?还是总能找到我放置的每两个框架的中间方向?

Also, would I be able to apply this to many frames or it would always find an intermediate orientation for each two frames I put in?

干杯

推荐答案

我不确定叠加图像的效果如何.从图像中消除模糊(包括在手持相机设备中应占主导地位的运动模糊)的另一种方法是通过盲反卷积.从根本上讲，这是一种方法，用于查找实际应用于实际图像(抖动照相机)的模糊滤镜的逆像.网络上有很多技术.在本文中，使用该算法的改进版本特别取得了良好的效果:

I'm not sure how well superimposing the images would work. Another way to remove blur (including motion blur which should dominate in handheld camera devices) from an image is by blind deconvolution. It is basically a method of finding the inverse of the blur filter that was physically applied (camera shaken) to the real image. There's plenty of techniques out on the web. I've specifically had good results using a modified version of the algorithm in this paper: http://www.cse.cuhk.edu.hk/~leojia/all_final_papers/motion_deblur_cvpr07.pdf

它在网络上的某个地方还带有可执行文件，因此您可以查看它是否适合您的目的.

It also comes with an executable file somewhere around the web so you can see if it's fit for your purpose.

祝你好运！

这篇关于用于OpenCV的抖动校正的图像校正的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！