11–20 of 35 posts
The biggest issue in real life use cases is lens distortion - these alignment algorithms usually don't correct barrel, pin-cushion or more complex lens distortions, making the alignment imperfect.
Image alignment allows for some fun image manipulations like these. I think one of the coolest novel applications of image alignment in recent years must be multi-frame super-resolution. For example, the Pixel phones use it to improve low-light and zoomed in photos[0]. [0] https://sites.google.com/view/handheld-super-res/
We ended up doing this by manually calibrating the images in matlab using a custom script and logging out a transformation matrix that we could then multiply the optical image by to get matching pixels in the thermal image.
Really fun project, but definitely a tedious thing to do, especially when its only a small part of the overall project. The project in the link also looks very cool, just not quite right for us as we probably wouldnt consistently have enough landmarks to map.
Image alignment allows for some fun image manipulations like these. I think one of the coolest novel applications of image alignment in recent years must be multi-frame super-resolution. For example, the Pixel phones use it to improve low-light and zoomed in photos[0]. [0] https://sites.google.com/view/handheld-super-res/
Cool! I'm planning on using image alignment for enhancing the spatial resolution of hyperspectral (HS) images, through the fusion of RGB and HS images.
Earlier quoted context omitted.
This is called drizzle (with dithering, e.g. the raw photos are taken with offsets) in the astrophotography sphere and is very effective when the images are undersampled. Although, astronomy images have the advantage of being filled with stars that are relatively easy to align very accurately.
'When undersampled' -- time to get a more modern sensor? 3.76um pixels + 800mm focal length is about 1 arcsecond per pixel, so that will end up seeing limited much of the time.
Also, drizzling was developed originally for the Hubble Deep Field, which isn't limited by seeing :).
Earlier quoted context omitted.
Cool! I'm planning on using image alignment for enhancing the spatial resolution of hyperspectral (HS) images, through the fusion of RGB and HS images.
You get something similar to a panchromatic image?
Image alignment allows for some fun image manipulations like these. I think one of the coolest novel applications of image alignment in recent years must be multi-frame super-resolution. For example, the Pixel phones use it to improve low-light and zoomed in photos[0]. [0] https://sites.google.com/view/handheld-super-res/
I currently working on an optical respiratory monitor and one of the challenges was image alignment/registration for an optical and a thermal image with wildly different resolutions, fovs. We ended up doing this by manually calibrating the images in matlab using a custom script and logging out a transformation matrix that we could then multiply the optical image by to get matching pixels in the thermal image. Really…