After finding the transformation parameter for successive image pair, we have to put all of them into one big images. Easy way to put all images into one mosaic is using superimposing method. For example, initially the mosaic is empty, then the first image is put into mosaic, then the second image is being put into mosaic where mosaic has empty pixel. If any pixel in the second images is mapped to a mosaic pixel which has not been already occupied by previous image pixel, then the value of that pixel (second image) is used the value of mapped pixel in mosaic. In this method, each pixel in mosaic takes it's value from only one image. This method gives unblured results but it has many artifact especially corner of the overlapping area because of misalignment. In this project, instead of superimposing method, we preferred to use pixel averaging method in blending phase.
Figure 1: Linear and exponential averaging scheme in image blending
In this project, we used three different methods based on averaging techniques. Especially, in order to reduce the artifacts, we have to give some weight to different part of images being blended together more heavily towards the center using linear or exponential weighting function. Also putting an upper-limit on number of different image pixel which is mapped to same location in mosaic while blending help us to reduce to the number of artifacts.
In the first method for blending,we give the weights of area in the image and weights are linearly increasing towards the center of the image. Therefore, if one pixel in blended image is mapped by two pixels from different images, the pixel which is closer to center is in own images contributes its color value more than the pixel which is closer to the border of own image. The second method differs from first by using exponential weighting instead of linear weighting. In our project, we use vertical blocks to give weight to images. In figure 1, you can see the linear and exponential weighs of these vertical image block. In the third method, we put some upper limit to the number of different image pixel contribute it's color onto same pixel in blended image. For example, if a pixel is contributed by 10 different images, then we choose 3 of them which has highest weight among the other.