Image Fusion Algorithm (Inductive chapter)

The complex follows several papers image fusion algorithms to induce the entire process to share with you (^_^).

The general process of the whole process of the panorama stitching method based on sift:

2. Preprocessing the image to be spliced. Mainly geometric correction and noise elimination . For geometric corrections. Because we are considering the real-time processing of the video, we just need to consider the full motion of the camera, which includes 8 degrees of freedom. Can be represented by a projection transform. H=[M0 M1 m2;m3 M4 M5;M6 M7 1], considering the complexity of its algorithm already has 3 times of N. We can consider reducing complexity by controlling the motion of the camera, such that the camera has only translational rotation and scaling, or affine transformations, reducing the complexity. To eliminate noise, consider that the dog itself is a very good band-pass filter. This step is omitted.

4. Extracting image Sift feature point descriptive narrative operator. Because the Gaussian function is the only possible scale-space kernel function, the entire SIFT algorithm is based on the "scale invariant" feature.

• Dog is the same way a person sees a scene from far and near two different places, and its edges don't change very much. Because its edge effect is very strong. Therefore, very much high frequency random noise is enhanced. This is the time to remove the inconsistent operator. A theoretical extremum point function is obtained by fitting three-dimensional two functions. At this point, you need to set a ratio that is greater than this ratio of points culling. After culling, a descriptive narrative operator is used to generate the characteristic points to adapt to the human eye.

• Then rotates the gradient direction of all the points in the area past the angle of the main direction determined above (to ensure rotational invariance).
6. The characteristic matching pair is obtained by the feature matching. It used to be the kd-tree algorithm, where we use approximate near-term neighbor algorithms, the BBF algorithm.

   This method uses a priority queue to make the search run from node to query node in from near order.

   (the BBF algorithm is more suitable for feature vectors with high dimensions.) The SIFT feature is a 128-dimensional vector, which is more useful )

8. Matching extraction, a robust feature matching pair is obtained. Because just using the RANSAC algorithm can lead to ghost phenomena. So we need to improve the RANSAC algorithm. The 90% of the registration error threshold determined by RANSAC is used as the registration threshold to extract the finally affine transformation matrix and enhance the robustness.
10. For image fusion. Because image fusion is for video preparation, real-time requirements are very high, so the high complexity of the algorithm is not applicable here.
• . This is very useful.
• Suppose we can improve the median filtering method. By reducing its execution time on the GPU, it can be used as a best-fit approach.

  There is also the ghost effect of eliminating stitching images, right now. Considering the image fusion method we are using, the probability of ghosting is not very high, so we can choose the simplest collage algorithm. collage algorithm is like collages that overlay overlapping areas with specific images to form an image. Strengths are simple and quick. But easy appears in the fault . So we need to use seam cost function standard to evaluate, to improve the stitching effect.

Note 2006 Michael Grabner simplifies the dog operator and reduces the computational complexity. The advantage of the dog operator is that it has very strong portability and universality . When K is set to 1.6, it is the approximation of the Laplace operator. In the dog algorithm, it is a kind of simulated retinal nerve extract information from the image to provide to the brain, which is approximately equal to 5 K.

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Image Fusion Algorithm (Inductive chapter)